Professor Hongyu Zhang

Logging has been a common practice for monitoring and diagnosing performance issues. However, logging comes at a cost, especially for large-scale online service systems. First, the overhead incurred by intensive logging is non-negligible. Second, it is costly to diagnose a performance issue if there are a tremendous amount of redundant logs. Therefore, we believe that it is important to limit the overhead incurred by logging, without sacrificing the logging effectiveness. In this paper we propose Log2, a cost-aware logging mechanism. Given a "budget" (defined as the maximum volume of logs allowed to be output in a time interval), Log2 makes the "whether to log" decision through a two-phase filtering mechanism. In the first phase, a large number of irrelevant logs are discarded efficiently. In the second phase, useful logs are cached and output while complying with logging budget. In this way, Log2 keeps the useful logs and discards the less useful ones. We have implemented Log2 and evaluated it on an open source system as well as a real-world online service system from Microsoft. The experimental results show that Log2 can control logging overhead while preserving logging effectiveness.

For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

Software crash is common. When a crash occurs, software developers can receive a report upon user permission. A crash report typically includes a call stack at the time of crash. An important step of debugging a crash is to identify faulty functions, which is often a tedious and labor-intensive task. In this paper, we propose CrashLocator, a method to locate faulty functions using the crash stack information in crash reports. It deduces possible crash traces (the failing execution traces that lead to crash) by expanding the crash stack with functions in static call graph. It then calculates the suspiciousness of each function in the approximate crash traces. The functions are then ranked by their suspiciousness scores and are recommended to developers for further investigation. We evaluate our approach using real-world Mozilla crash data. The results show that our approach is effective: We can locate 50.6%, 63.7% and 67.5% of crashing faults by examining top 1, 5 and 10 functions recommended by CrashLocator, respectively. Our approach outperforms the conventional stack-only methods significantly.

Software often crashes despite tremendous effort on software quality assurance. Once developers receive a crash report, they need to reproduce the crash in order to understand the problem and locate the fault. However, limited information from crash reports often makes crash reproduction difficult. Many "captureand-replay" techniques have been proposed to automatically capture program execution data from the failing code, and help developers replay the crash scenarios based on the captured data. However, such techniques often suffer from heavy overhead and introduce privacy concerns. Recently, methods such as BugRedux were proposed to generate test input that leads to crash through symbolic execution. However, such methods have inherent limitations because they rely on conventional symbolic execution techniques. In this paper, we propose a dynamic symbolic execution method called SymCon, which addresses the limitation of conventional symbolic execution by selecting functions that are hard to be resolved by a constraint solver and using their concrete runtime values to replace the symbols. We then propose SymCrash, a selective recording approach that only instruments and monitors the hard-to-solve functions. SymCrash can generate test input for crashes through SymCon. We have applied our approach to successfully reproduce 13 failures of 6 real-world programs. Our results confirm that the proposed approach is suitable for reproducing crashes, in terms of effectiveness, overhead, and privacy. It also outperforms the related methods.

We propose a pattern-based approach to effectively and efficiently analyzing sequential software engineering (SE) data. Different from other types of SE data, sequential SE data preserves unique temporal properties, which cannot be easily analyzed without much programming effort. In order to facilitate the analysis of sequential SE data, we design a sequential pattern query language (SPQL), which specifies the temporal properties based on regular expressions, and is enhanced with variables and statements to store and manipulate matching states. We also propose a query engine to effectively process the SPQL queries. We have applied our approach to analyze two types of SE data, namely bug report history and source code change history. We experiment with 181,213 Eclipse bug reports and 323,989 code revisions of Android. SPQL enables us to explore interesting temporal properties underneath these sequential data with a few lines of query code and low matching overhead. The analysis results can help better understand a software process and identify process violations.

For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of 'who fixed what, where'. For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.

Ideally, software defect prediction models should help organize software quality assurance (SQA) resources and reduce cost of finding defects by allowing the modules most likely to contain defects to be inspected first. In this paper, we study the cost-effectiveness of applying defect prediction models in SQA and propose a basic cost-effectiveness criterion. The criterion implies that defect prediction models should be applied with caution. We also propose a new metric FN/(FN+TN) to measure the cost-effectiveness of a defect prediction model. Copyright 2013 ACM.

A large and complex software system could contain a large number of bugs. It is desirable for developers to understand how these bugs are distributed across the system, so they could have a better overview of software quality. In this paper, we describe BugMap, a tool we developed for visualizing large-scale bug location information. Taken source code and bug data as the input, BugMap can display bug localizations on a topographic map. By examining the topographic map, developers can understand how the components and files are affected by bugs. We apply this tool to visualize the distribution of Eclipse bugs across components/files. The results show that our tool is effective for understanding the overall quality status of a large-scale system and for identifying the problematic areas of the system. Copyright 2013 ACM.

In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests. © 2013 Springer-Verlag Berlin Heidelberg.

During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice. © 2013 IEEE.

For a large and complex software system, the project team could receive a large number of bug reports. Some bug reports could be duplicates as they essentially report the same problem. It is often tedious and costly to manually check if a newly reported bug is a duplicate of an already reported bug. In this paper, we propose BugSim, a method that can automatically retrieve duplicate bug reports given a new bug report. BugSim is based on learning to rank concepts. We identify textual and statistical features of bug reports and propose a similarity function for bug reports based on the features. We then construct a training set by assembling pairs of duplicate and non-duplicate bug reports. We train the weights of features by applying the stochastic gradient descent algorithm over the training set. For a new bug report, we retrieve candidate duplicate reports using the trained model. We evaluate BugSim using more than 45,100 real bug reports of twelve Eclipse projects. The evaluation results show that the proposed method is effective. On average, the recall rate for the top 10 retrieved reports is 76.11%. Furthermore, BugSim outperforms the previous state-of-art methods that are implemented using SVM and BM25F ext. © 2012 ACM.

Buffer overflow vulnerability is one of the major security threats for applications written in C/C++. Among the existing approaches for detecting buffer overflow vulnerability, though flow sensitive based approaches offer higher precision but they are limited by heavy overhead and the fact that many constraints are unsolvable. We propose a novel method to efficiently detect vulnerable buffer overflows in any given control flow graph through recognizing two patterns. The proposed approach first uses syntax analysis to filter away those branches that cannot possibly comply with any of the two patterns before applying a limited symbolic evaluation for a precise matching against the patterns. The proposed approach only needs to evaluate a limited set of selected branch predicates according to the patterns and avoids the need to deal with a large number of general branch predicates. This significantly improves the scalability while not sacrificing the detection precision. Our experiments demonstrate the scalability and efficiency of the proposed method, which demonstrates its applicability. © 2012 IEEE.

Welcome to WETSoM2012, the 3rd International Workshop on Emerging Trends in Software Metrics. Since its start, WETSoM attracted a blend of academic and industrial researchers, creating a stimulating atmosphere to discuss the progresses of software metrics. A key motivation for this workshop is to help overcoming the low impact that software metrics has on current software development. This is pursued by critically examining the evidence for the effectiveness of existing metrics and identifying new directions for metrics. Evidence for existing metrics includes how the metrics have been used in practice and studies showing their effectiveness. Identifying new directions includes use of new theories, such as complex network theory, on which to base metrics. We are pleased that this year WETSoMfeatures 12 technical paper and an exciting keynote on mining developers' communication to assess software quality by Massimiliano di Penta. The program of WETSoM2012 is the result of hard work by many dedicated people; we especially thank the authors of submitted papers and the members of the program committee. Above all, the greatest richness of this workshop is its participants, who shape the discussion and points into new directions for software metrics research and practice. We hope you will have a great time and an unforgettable experience at WETSoM2012. © 2012 IEEE.

We present the application of software process modeling and simulation using an agent-based approach to a real case study of software maintenance. The original process used PSP/TSP; it spent a large amount of time estimating in advance maintenance requests, and needed to be greatly improved. To this purpose, a Kanban system was successfully implemented, that demonstrated to be able to substantially improve the process without giving up PSP/TSP. We customized the simulator and, using input data with the same characteristics of the real ones, we were able to obtain results very similar to that of the processes of the case study, in particular of the original process. We also simulated, using the same input data, the possible application of the Scrum process to the same data, showing results comparable to the Kanban process. © 2012 Springer-Verlag Berlin Heidelberg.

A plethora of software engineering data have been produced by different organizations and tools over time. These data may come from different sources, and are often disparate and distributed. The integration of these data may open up the possibility of conducting systemic, holistic study of software projects in ways previously unexplored. Semantic Web technologies have been used successfully in a wide array of domains such as health care and life sciences as a platform for information integration and knowledge management. The success is largely due to the open and extensible nature of ontology languages as well as growing tool support. We believe that Semantic Web technologies represent an ideal platform for the integration of software engineering data in a semantic repository. By querying and analyzing such a repository, researchers and practitioners can better understand and control software engineering activities and processes. In this paper, we describe how we apply Semantic Web techniques to integrate object-oriented software engineering data from different sources. We also show how the integrated data can help us answer complex queries about large-scale software projects through a case study on the Eclipse system. © 2011 ACM.

Software defect information, including links between bugs and committed changes, plays an important role in software maintenance such as measuring quality and predicting defects. Usually, the links are automatically mined from change logs and bug reports using heuristics such as searching for specific keywords and bug IDs in change logs. However, the accuracy of these heuristics depends on the quality of change logs. Bird et al. found that there are many missing links due to the absence of bug references in change logs. They also found that the missing links lead to biased defect information, and it affects defect prediction performance. We manually inspected the explicit links, which have explicit bug IDs in change logs and observed that the links exhibit certain features. Based on our observation, we developed an automatic link recovery algorithm, ReLink, which automatically learns criteria of features from explicit links to recover missing links. We applied ReLink to three open source projects. ReLink reliably identified links with 89% precision and 78% recall on average, while the traditional heuristics alone achieve 91% precision and 64% recall. We also evaluated the impact of recovered links on software maintainability measurement and defect prediction, and found the results of ReLink yields significantly better accuracy than those of traditional heuristics. © 2011 ACM.

Database application is one of the most common types of systems. Grounded on the simple concept of data lifecycle-any data in database is created from insertion, used via selection and modification and terminated at deletion-this paper proposes a novel approach to reverse engineer the data lifecycle automatically from the source code of database applications. The extracted information can be used for the selection of open-source database applications for adaptation. It can also be used for maintenance and verification of database applications. A tool has been developed to implement the proposed approach for PHP-based database applications. Case studies have also been conducted to evaluate the use of the proposed approach.

Product Lines for mission critical Command and Control systems was a starting point for a long lasting research collaboration between National University of Singapore (NUS) and ST Electronics (Info-Software Systems) Pte Ltd (STEE-InfoSoft). Collaboration was intensified by a joint research project, also involving University of Waterloo and Netron Inc. that led to development of reuse technology called XVCL. The contribution of this paper is twofold: First, we describe collaboration modes, factors that were critical to sustain collaboration, and benefits for university and industry gained over years. Among the main benefits, STEE-InfoSoft advanced its reuse practice by applying XVCL in several software Product Line projects, while NUS team received early feedback from STEE-InfoSoft which helped refine XVCL reuse methods and keep academic research in sync with industrial realities. Academic findings and industrial pilots have opened new unexpected research directions. Second, we draw lessons learned from many projects, to explain the general nature and significance of problems addressed with the XVCL approach. © 2011 Springer-Verlag.

BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusing on the defect-rich portions of the training sets. METHOD: Defect data CM1, KC1, MC1, PC1, PC3 was separated into components. A subset of the projects (selected at random) were set aside for testing. Training sets were generated for a NaiveBayes classifier in two ways. In sample the dense treatment, the components with higher than the median number of defective modules were used for training. In the standard treatment, modules from any component were used for training. Both samples were run against the test set and evaluated using recall, probability of false alarm, and precision. In addition, under sampling and over sampling was performed on the defect data. Each method was repeated in a 10-by-10 cross-validation experiment. RESULTS: Prediction models learned from defect dense components out-performed standard method, under sampling, as well as over sampling. In statistical rankings based on recall, probability of false alarm, and precision, models learned from dense components won 4-5 times more often than any other method, and also lost the least amount of times. CONCLUSIONS: Given training data where most of the defects exist in small numbers of components, better defect predictors can be trained from the defect dense components.

The Workshop on Emerging Trends in Software Metrics aims at bringing together researchers and practitioners to discuss the progress of software metrics. The motivation for this workshop is the low impact that software metrics has on current software development. The goals of this workshop are to critically examine the evidence for the effectiveness of existing metrics and to identify new directions for development of software metrics. © 2010 ACM.

We analyze the Eclipse defect data from June 2004 to November 2007, and find that the growth of the number of defects can be well modeled by polynomial functions. Furthermore, we can predict the number of future Eclipse defects based on the nature of defect growth. Copyright 2008 ACM.

Semantic web ontology languages, such as OWL, have been widely used for knowledge representation. Through empirical analysis of real-world ontologies we discover that, like many natural and social phenomenon, the semantic web ontology is also "scale-free".

We perform an empirical study of class sizes (in terms of Lines of Code) on a number of large Java software systems, and discover an interesting pattern - that many classes have only small sizes whereas a few classes have large size. We call this phenomenon the small class phenomenon. Further analysis shows that the class sizes follow the lognormal distribution. Having understood the distribution of class sizes, we then derive a general size estimation model, which reveals the relationship between the size of a large Java system and the number oficiasses the system has. In this paper, we also show that the adoption of objectorientation is a possible cause of the small class phenomenon. We believe our study reveals the regularity that emerges from large-scale object-oriented software construction, and hope our research can contribute to a deep understanding of computer programming. © 2007 IEEE.

The ability to predict defective modules can help us allocate limited quality assurance resources effectively and efficiently. In this paper, we propose a complexitybased method for predicting defect-prone components. Our method takes three code-level complexity measures as input, namely Lines of Code, McCabe's Cyclomatic Complexity and Halstead's Volume, and classifies components as either defective or non-defective. We perform an extensive study of twelve classification models using the public NASA dataseis. Cross-validation results show that our method can achieve good prediction accuracy. This study confirms that static code complexity measures can be useful indicators of component quality. © 2007 IEEE.

We set up an experiment to evaluate JEE as a platform for product line development. While JEE provides many useful mechanisms for reuse of common services/components, still we found that systematic across-the-board reuse in application domain-specific areas was hard. The main difficulty was the lack of a mechanism to represent groups of similar components in a generic, adaptable form. Such similar components arise as the number of variant features of a product line grows, and we need to accommodate legal combinations of variant features in components of a product line architecture. Such uncontrolled growth of similar component versions hinders productivity of reuse-based development and raises maintenance costs. In the paper, we study the manifestation of this problem in the JEE¿ database access layer. Interactive Development Environments such as NetBeans or JBuilder speed up the development process, but they do not address the source of the problem, which is the lack of mechanisms to design generic components capable of accommodating variant features in various combinations. We filled this gap with a "mixed strategy" solution based on generative programming technique of XVCL applied on top of JEE. In the paper, we highlight the nature of the problems we encountered and our solution. Copyright © (2007) by Knowledge Systems Institute (KSI).

Effort and cost estimation is crucial in software management. Estimation of software size plays a key role in the estimation. Line of Code (LOG) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. Conceptual data model is widely used in the requirements analysis for information systems. It is also not difficult to construct conceptual data models in the early stage of developing information systems. Much characteristic of an information system is actually reflected from its conceptual data model. We explore into the use of conceptual data model for estimating LOC. This paper proposes a novel method for estimating LOG for an information system from its conceptual data model through the use of multiple linear regression model. We have validated the method through collecting samples from both the industry and open-source systems. Copyright 2006 ACM.

Meta-programs are generic, incomplete, adaptable programs that are instantiated at construction time to meet specific requirements. Templates and generative techniques are examples of meta-programming techniques. Understanding of meta-programs is more difficult than understanding of concrete, executable programs. Static and dynamic analysis methods have been applied to ease understanding of programs - can similar methods be used for meta-programs? In our projects, we build meta-programs with a meta-programming technique called XVCL. Meta-programs in XVCL are organized into a hierarchy of meta-components from which the XVCL processor generates concrete, executable programs that meet specific requirements. We developed an automated system that analyzes XVCL meta-programs, and presents developers with information that helps them work with meta-programs more effectively. Our system conducts both static and dynamic analysis of a. meta-program. An integral part of our solution is a query language, FQL in which we formulate questions about meta-prograin properties. An FQL query processor automatically answers a class of queries. The analysis method described in the paper is specific to XVCL. However, the principle of our approach can be applied to other meta-programming systems. We believe readers interested in metaprogramming in general will find some of the lessons from our experiment interesting and useful. © World Scientific Publishing Company.

Research on features has received much attention in the domain engineering community. Feature modeling plays an important role in the design and implementation of complex software systems. However, the presentation and analysis of feature models are still largely informal. There is also an increasing need for methods and tools that can support automated feature model analysis. This paper presents a formal engineering approach to the specification and verification of feature models. A formal semantics for the feature modeling language is defined using first-order logic. It provides a precise and rigorous formal interpretation for the graphical notation. In addition, further validation of the semantics using the Z/EVES theorem prover is presented. Finally, we demonstrate that the consistency of a feature model and its configurations can be automatically verified by encoding the semantics into the Alloy Analyzer. A case study of the Key Word in Context (KWIC) index systems feature model is presented to illustrate the verification process. © 2005 IEEE.

We developed XVCL (XML-based Variant Configuration Language), a method and tool for product lines, to facilitate handling variants in reusable software assets (such as architecture, code components or UML models). XVCL is a newer version of Bassett's frames [1], a technology that has achieved substantial productivity improvements in large data processing product lines written in COBOL. Despite its simplicity, XVCL can effectively manage a wide range of product line variants from a compact base of meta-components, structured for effective reuse. We applied XVCL in two medium-size product line projects and a number of smaller case studies. In this paper, we communicate XVCL's capabilities to support product lines by means of a simple, but still interesting, example of the KWIC system introduced by Parnas in 1970's. We show how we can handle functional variants, variant design decisions and implementation-level variants in a generic KWIC system.

Domain models describe common and variant requirements for a family of similar systems. Although most of the notations, such as UML, are meant for modeling a single system, they can be extended to model variants. We have done that and applied such extended notations in our projects. We soon found that our models with variants were becoming overly complicated, undermining the major role of domain analysis which is understanding. One variant was often reflected in many models and any given model was affected by many variants. The number of possible variant combinations was growing rapidly and mutual dependencies among variants even further complicated the domain model. We realized that our purely descriptive domain model was only useful for small examples but it did not scale up. In this paper, we describe a modeling method and a Flexible Variant Configuration tool (FVC for short) that alleviate the above mentioned problems. In our approach, we start by modeling so-called domain defaults, i.e., requirements that characterize a typical system in a domain. Then, we describe variants as deltas in respect to domain defaults. The FVC interprets variants to produce customized domain model views for a system that meets specific requirements. We implemented the above concepts using commercial tools Netron Fusion¿ and Rational Rose¿. In the paper, we illustrate our domain modeling method and tool with examples from the Facility Reservation System domain. © 2003 Elsevier Inc. All rights reserved.

XML-based Variant Configuration Language (XVCL) is a meta-programming technique and tool that provides effective reuse mechanisms. It includes a methodology and a tool-the XVCL processor. The methodology shows how to discover the structure of the solution for the application domain and for the types of variants one wants to address. The XVCL processor automates the routine yet error-prone program construction tasks, allowing to focus on what is novel about the problem domains, requiring creativity.

XVCL (XML-based Variant Configuration Language) is a general-purpose mark-up language for configuring variants in programs and other types of documents. We can apply XVCL to configure variants in a variety of software assets such as software architecture, program code, test cases, technical and user-level program documentation or requirement specifications. The principles of the XVCL have been thoroughly tested in practice. XVCL is based on the same concepts as the frame technology [1]. Frame technology has been extensively applied in industry to manage variants and evolve multi-million-line, COBOL-based, information systems. An independent analysis showed that frame technology has reduced large software project costs by over 84% and their times-to-market by 70%, when compared to industry norms [1, 2]. At the same time, we found that the principles of XVCL are not easy to communicate. In this paper, we describe a subset of XVCL. We trust this subset of XVCL is easy to understand and still effectively communicates essential XVCL concepts. To illustrate the XVCL method, we further describe an XVCL solution to handling variants in a Notepad system. Copyright 2002 ACM.

A quantitative study has shown that frame technology [1] supported by Fusion¿ toolset can lead to reduction in time-to-market (70%) and project costs (84%). Frame technology has been developed to handle large COBOL-based business software product families. We wished to investigate how the principle of frame approach can be applied to support product families in other application domains, in particular to build distributed component-based systems written in Object-Oriented languages. As Fusion¿ is tightly coupled with COBOL, we implemented our own tools based on frame concepts using the XML technology. In our solution, a generic architecture for a product family is a hierarchy of XML documents. Each such document contains a reusable program fragment instrumented for change with XML tags. We use a tool built on top of XML parsing framework JAXP to process documents in order to produce a custom member of a product family. Our solution is cost-effective and extensible. In the paper, we describe our solution, illustrating its use with examples. We intend to make our solution available to public in order to encourage investigation of frame concepts in other application domains, implementation languages and platforms.

In this paper, we describe an XML-based language, called XVCL, for managing variants in component-based product families. Using XVCL, we can organize product family assets and instrument them to accommodate variants. A tool that interprets XVCL and provides semi-automatic support for asset customization is also introduced. In our projects, we applied XVCL to manage variants in UML domain models and in generic architectures for product families. We have achieved simple forms of separation of concerns (in both models and architectures) and we are investigating advanced forms in current work. We plan to compare XVCL to other emerging techniques that lead to separating of concerns in software models, documents, architectures and code.

A domain model describes common and variant requirements for a system family. UML notations used in requirements analysis and software modeling can be extended with "variation points" to cater for variant requirements. However, UML models for a large single system are already complicated enough. With variants - UML domain models soon become too complicated to be useful. The main reasons are the explosion of possible variant combinations, complex dependencies among variants and inability to trace variants from a domain model down to the requirements for a specific system, member of a family. We believe that the above mentioned problems cannot be solved at the domain model description level alone. In the paper, we propose a novel solution based on a tool that interprets and manipulates domain models to provide analysts with customized, simple domain views. We describe a variant configuration language that allows us to instrument domain models with variation points and record variant dependencies. An interpreter of this language produces customized views of a domain model, helping analysts understand and reuse software models. We describe the concept of our approach and its simple implementation based on XML and XMI technologies.

The core goal of software defect prediction (SDP) is to identify modules with a high likelihood of defects, thereby enabling prioritization of quality assurance activities with low inspection effort. There are many supervised defect prediction models that are extensively studied. However, these methods require the need for labeling data to get enough training modules, which will cause a lot of waste of human resources. Cross-project defect prediction primarily reuses models trained on other projects with enough historical data. However, this strategy is often hindered by large distribution differences across different projects and privacy concerns of data. Unsupervised learning technique is an alternative solution to the unlabeled data, but it mainly focuses on single-view prediction by concatenating all the software metrics. This ignores the diversity and complementarity of different types of metrics. This study proposes a novel approach, namely, multiview unsupervised software defect prediction (MUSDP). It aims to collaboratively learn the diversity and complementarity of different views to build a robust and reliable defect prediction model. Extensive experiments on 28 releases from eight software projects indicate that MUSDP exhibits superior or comparable results regarding G-mean, AUC, Popt, and Recall@20% compared to competing supervised and unsupervised methods. For the interpretation of MUSDP, the number of added and deleted lines significantly influence its predictions.

Context: User reviews of mobile apps provide an important communication channel between developers and users. Existing approaches to automated app review analysis mainly focus on one task (e.g., bug classification task, information extraction task, etc.) at a time, and are often constrained by the manually defined patterns and the ignorance of the correlations among the tasks. Recently, multi-task learning (MTL) has been successfully applied in many scenarios, with the potential to address the limitations associated with app review mining tasks. Objective: In this paper, we propose MABLE, a deep MTL-based and semantic-aware approach, to improve app review analysis by exploiting task correlations. Methods: MABLE jointly identifies the types of involved bugs reported in the review and extracts the fine-grained features where bugs might occur. It consists of three main phases: (1) data preparation phase, which prepares data to allow data sharing beyond single task learning; (2) model construction phase, which employs a BERT model as the shared representation layer to capture the semantic meanings of reviews, and task-specific layers to model two tasks in parallel; (3) model training phase, which enables eavesdropping by shared loss function between the two related tasks. Results: Evaluation results on six apps show that MABLE outperforms ten commonly-used and state-of-the-art baselines, with the precision of 79.76% and the recall of 79.24% for classifying bugs, and the precision of 79.83% and the recall of 80.33% for extracting problematic app features. The MTL mechanism improves the F-measure of two tasks by 3.80% and 4.63%, respectively. Conclusion: The proposed approach provides a novel and effective way to jointly learn two related review analysis tasks, and sheds light on exploring other review mining tasks.

Reusing APIs can greatly expedite the software development process and reduce programming effort. To learn how to use APIs, developers often rely on API learning resources (such as API references and tutorials) that contain rich and valuable API knowledge. In recent years, numerous API analytic approaches have been presented to help developers mine API knowledge from API learning resources. While these approaches have shown promising results in various tasks, there are many opportunities in this area. In this paper, we discuss several possible future works on API analytics.

Just-in-time software defect prediction (JIT-SDP) is a fine-grained, easy-to-trace, and practical method. Unfortunately, JIT-SDP usually suffers from the class imbalance problem, which affects the performance of the models. Data sampling is one of the commonly used class imbalance techniques to overcome this problem. However, there is a lack of comprehensive empirical studies to compare different data sampling techniques on the performance of JIT-SDP. In this paper, we consider both defect classification and defect ranking, two typical application scenarios. To this end, we performed an empirical comparison of 10 data sampling algorithms on the performance of JIT-SDP. Extensive experiments on 10 open-source projects with 12 performance measures show that the effectiveness of data sampling techniques can indeed vary relying on the specific evaluation measures in both defect classification and defect ranking scenarios. Specifically, the RUM algorithm has demonstrated superior performance overall in the context of defect classification, particularly in F-measure, AUC, and MCC. On the other hand, for defect ranking, the ENN algorithm has emerged as the most favorable option, exhibiting perfect results in Popt, Recall@20%, and F-measure@20%. However, data sampling techniques can lead to an increase in false alarms and require the inspection of a higher number of changes. These findings highlight the importance of carefully selecting the appropriate data sampling technique based on the specific evaluation measures for different scenarios.

Recommender systems (RSs) aim to provide suggestions for items that are most pertinent to a particular user. Typically, RSs are trained and evaluated directly on the observed items, raising concerns about exposure bias - many missing items are false negatives, which were not consumed due to lack of exposure rather than lack of affinity. In addition, user satisfaction is often ignored in previous RSs, where consumer behaviors may be influenced by advertisement or promotion instead of actual user interests. In this paper, we propose a novel model-agnostic causal inference method for debiased recommendation, which models item Exposure and user Satisfaction simultaneously with Causal Inference (ESCI). Specifically, we formulate a causal graph to describe the recommendation process, where the ranking score is influenced by item exposure, user satisfaction, and user-item matching. We investigate the change in the ranking score when item exposure is discarded. In addition, we propose an adversarial training strategy to improve the generalization and robustness of recommender systems. During testing, we perform causal inference to remove the effect of item exposure. The comprehensive experimental study on four benchmark datasets demonstrates that the proposed ESCI enhances recommendation performance for users with non-high interaction frequencies, thereby outperforming state-of-the-art baselines.

With the widespread success of deep learning technologies, many trained deep neural network (DNN) models are now publicly available. However, directly reusing the public DNN models for new tasks often fails due to mismatching functionality or performance. Inspired by the notion of modularization and composition in software reuse, we investigate the possibility of improving the reusability of DNN models in a more fine-grained manner. Specifically, we propose two modularization approaches named CNNSplitter and GradSplitter, which can decompose a trained convolutional neural network (CNN) model for N-class classification into N small reusable modules. Each module recognizes one of the N classes and contains a part of the convolution kernels of the trained CNN model. Then, the resulting modules can be reused to patch existing CNN models or build new CNN models through composition. The main difference between CNNSplitter and GradSplitter lies in their search methods: the former relies on a genetic algorithm to explore search space, while the latter utilizes a gradient-based search method. Our experiments with three representative CNNs on three widely used public datasets demonstrate the effectiveness of the proposed approaches. Compared with CNNSplitter, GradSplitter incurs less accuracy loss, produces much smaller modules (19.88% fewer kernels), and achieves better results on patching weak models. In particular, experiments on GradSplitter show that (1) by patching weak models, the average improvement in terms of precision, recall, and F1-score is 17.13%, 4.95%, and 11.47%, respectively, and (2) for a new task, compared with the models trained from scratch, reusing modules achieves similar accuracy (the average loss of accuracy is only 2.46%) without a costly training process. Our approaches provide a viable solution to the rapid development and improvement of CNN models.

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.

API tutorials are crucial resources as they often provide detailed explanations of how to utilize APIs. Typically, an API tutorial is segmented into a number of consecutive fragments. If a fragment explains API usage, we regard it as a relevant fragment of the API. Recognizing relevant fragments can aid developers in comprehending, learning, and using APIs. Recently, some studies have presented relevant fragments recognition approaches, which mainly focused on using API tutorials or Stack Overflow to train the recognition model. API references are also important API learning resources as they contain abundant API knowledge. Considering the similarity between API tutorials and API references (both provide API knowledge), we believe that using API knowledge from API references could help recognize relevant tutorial fragments of APIs effectively. However, it is non-trivial to leverage API references to build a supervised learning-based recognition model. Two major problems are the lack of labeled API references and the unavailability of engineered features of API references. We propose a supervised learning based approach named RRTR (which stands for Recognize Relevant Tutorial fragments using API References) to address the above problems. For the problem of lacking labeled API references, RRTR designs heuristic rules to automatically collect relevant and irrelevant API references for APIs. Regarding the unavailable engineered features issue, we adopt the pre-trained SBERT model (SBERT stands for Sentence-BERT) to automatically learn semantic features for API references. More specifically, we first automatically generate labeled ( API, ARE) pairs (ARE stands for an API reference) via our heuristic rules of API references. We then use SBERT to automatically learn semantic features for the collected pairs and train a supervised learning based recognition model. Finally, we can recognize the relevant tutorial fragments of APIs based on the trained model. To evaluate the effectiveness of RRTR, we collected Java and Android API reference datasets containing a total of 20,680 labeled ( API, ARE) pairs. Experimental results demonstrate that RRTR outperforms state-of-the-art approaches in terms of F-Measure on two datasets. In addition, we conducted a user study to investigate the practicality of RRTR and the results further illustrate the effectiveness of RRTR in practice. The proposed RRTR approach can effectively recognize relevant fragments of APIs with API references by solving the problems of lacking labeled API references and engineered features of API references.

Test-to-code traceability links (TCTLs) establish links between test artifacts and code artifacts. These links enable developers and testers to quickly identify the specific pieces of code tested by particular test cases, thus facilitating more efficient debugging, regression testing, and maintenance activities. Various approaches, based on distinct concepts, have been proposed to establish method-level TCTLs, specifically linking unit tests to corresponding focal methods. Static methods, such as naming-convention-based methods, use heuristic- and similarity-based strategies. However, such methods face the following challenges: Developers, driven by specific scenarios and development requirements, may deviate from naming conventions, leading to TCTL identification failures. Static methods often overlook the rich semantics embedded within tests, leading to erroneous associations between tests and semantically unrelated code fragments. Although dynamic methods achieve promising results, they require the project to be compilable and the tests to be executable, limiting their usability. This limitation is significant for downstream tasks requiring massive test-code pairs, as not all projects can meet these requirements. To tackle the abovementioned limitations, we propose a novel static method-level TCTL approach, named TestLinker. For the first challenge of existing static approaches, TestLinker introduces a two-phase TCTL framework to accommodate different project types in a triage manner. As for the second challenge, we employ the semantic correlation learning, which learns and establishes the semantic correlations between tests and focal methods based on Pre-trained Code Models (PCMs). TestLinker further establishes mapping rules to accurately link the recommended function name to the concrete production function declaration. Empirical evaluation on a meticulously labeled dataset reveals that TestLinker significantly outperforms traditional static techniques, showing average F1-score improvements ranging from 73.48% to 202.00%. Moreover, compared to state-of-the-art dynamic methods, TestLinker, which only leverages static information, demonstrates comparable or even better performance, with an average F1-score increase of 37.40%.

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods.

With the rapid development of deep learning (DL), the recent trend of log-based anomaly detection focuses on extracting semantic information from log events (i.e., templates of log messages) and designing more advanced DL models for anomaly detection. Indeed, the effectiveness of log-based anomaly detection can be improved, but these DL-based techniques further suffer from the limitations of more heavy dependency on training data (such as data quality or data labels) and higher costs in time and resources due to the complexity and scale of DL models, which hinder their practical use. On the contrary, the techniques based on traditional machine learning or data mining algorithms are less dependent on training data and more efficient but produce worse effectiveness than DL-based techniques, which is mainly caused by the problem of unseen log events (some log events in incoming log messages are unseen in training data) confirmed by our motivating study. Intuitively, if we can improve the effectiveness of traditional techniques to be comparable with advanced DL-based techniques, then log-based anomaly detection can be more practical. Indeed, an existing study in the other area (i.e., linking questions posted on Stack Overflow) has pointed out that traditional techniques with some optimizations can indeed achieve comparable effectiveness with the state-of-the-art DL-based technique, indicating the feasibility of enhancing traditional log-based anomaly detection techniques to some degree.Inspired by the idea of "try-with-simpler,"we conducted the first empirical study to explore the potential of improving traditional techniques for more practical log-based anomaly detection. In this work, we optimized the traditional unsupervised PCA (Principal Component Analysis) technique by incorporating a lightweight semantic-based log representation in it, called SemPCA, and conducted an extensive study to investigate the potential of SemPCA for more practical log-based anomaly detection. By comparing seven log-based anomaly detection techniques (including four DL-based techniques, two traditional techniques, and SemPCA) on both public and industrial datasets, our results show that SemPCA achieves comparable effectiveness as advanced supervised/semi-supervised DL-based techniques while being much more stable under insufficient training data and more efficient, demonstrating that the traditional technique can still excel after small but useful adaptation.

Cross-project defect prediction (CPDP) refers to recognizing defective software modules in one project (i.e., target) using historical data collected from other projects (i.e., source), which can help developers find defects and prioritize their testing efforts. Unfortunately, there often exists large distribution difference between the source and target data. Most CPDP methods neglect to select the appropriate source data for a given target at the project level. More importantly, existing CPDP models are parametric methods, which usually require intensive parameter selection and tuning to achieve better prediction performance. This would hinder wide applicability of CPDP in practice. Moreover, most CPDP methods do not address the cross-project class imbalance problem. These limitations lead to suboptimal CPDP results. In this paper, we propose a novel data selection and sampling based domain programming predictor (DSSDPP) for CPDP, which addresses the above limitations. DSSDPP is a non-parametric CPDP method, which can perform knowledge transfer across projects without the need for parameter selection and tuning. By exploiting the structures of source and target data, DSSDPP can learn a discriminative transfer classifier for identifying defects of the target project. Extensive experiments on 22 projects from four datasets indicate that DSSDPP achieves better MCC and AUC results against a range of competing methods both in the single-source and multi-source scenarios. Since DSSDPP is easy, effective, extensible, and efficient, we suggest that future work can use it with the well-chosen source data to conduct CPDP especially for the projects with limited computational budget.

Large-scale software-intensive systems often generate logs for troubleshooting purpose. The system logs are semi-structured text messages that record the internal status of a system at runtime. In this paper, we propose ADR (Anomaly Detection by workflow Relations), which can mine numerical relations from logs and then utilize the discovered relations to detect system anomalies. Firstly the raw log entries are parsed into sequences of log events and transformed to an extended event-count-matrix. The relations among the matrix columns represent the relations among the system events in workflows. Next, ADR evaluates the matrix's nullspace that corresponds to the linearly dependent relations of the columns. Anomalies can be detected by evaluating whether or not the logs violate the mined relations. We design two types of ADR: sADR (for semi-supervised learning) and uADR (for unsupervised learning). We have evaluated them on four public log datasets. The experimental results show that ADR can extract the workflow relations from log data, and is effective for log-based anomaly detection in both semi-supervised and unsupervised manners.

Pre-trained models have been shown effective in many code intelligence tasks, such as automatic code summarization and defect prediction. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the inputs to pre-training and downstream tasks are in different forms, it is hard to fully explore the knowledge of pre-trained models. Besides, the performance of fine-tuning strongly relies on the amount of downstream task data, while in practice, the data scarcity scenarios are common. Recent studies in the natural language processing (NLP) field show that prompt tuning, a new paradigm for tuning, alleviates the above issues and achieves promising results in various NLP tasks. In prompt tuning, the prompts inserted during tuning provide task-specific knowledge, which is especially beneficial for tasks with relatively scarce data. In this article, we empirically evaluate the usage and effect of prompt tuning in code intelligence tasks. We conduct prompt tuning on popular pre-trained models CodeBERT and CodeT5 and experiment with four code intelligence tasks including defect prediction, code search, code summarization, and code translation. Our experimental results show that prompt tuning consistently outperforms fine-tuning in all four tasks. In addition, prompt tuning shows great potential in low-resource scenarios, e.g., improving the BLEU scores of fine-tuning by more than 26% on average for code summarization. Our results suggest that instead of fine-tuning, we could adapt prompt tuning for code intelligence tasks to achieve better performance, especially when lacking task-specific data. We also discuss the implications for adapting prompt tuning in code intelligence tasks.

Code summarization aims to generate concise natural language descriptions for a piece of code, which can help developers comprehend the source code. Analysis of current work shows that the extraction of syntactic and semantic features of source code is crucial for generating high-quality summaries. To provide a more comprehensive feature representation of source code from different perspectives, we propose an approach named EnCoSum, which enhances semantic features for the multi-scale multi-modal code summarization method. This method complements our previously proposed M2TS approach (multi-scale multi-modal approach based on Transformer for source code summarization), which uses the multi-scale method to capture Abstract Syntax Trees (ASTs) structural information more completely and accurately at multiple local and global levels. In addition, we devise a new cross-modal fusion method to fuse source code and AST features, which can highlight key features in each modality that help generate summaries. To obtain richer semantic information, we improve M2TS. First, we add data flow and control flow to ASTs, and added-edge ASTs, called Enhanced-ASTs (E-ASTs). In addition, we introduce method name sequences extracted in the source code, which exist more knowledge about critical tokens in the corresponding summaries and can help the model generate higher-quality summaries. We conduct extensive experiments on processed Java and Python datasets and evaluate our approach via the four most commonly used machine translation metrics. The experimental results demonstrate that EnCoSum is effective and outperforms current state-of-the-art methods. Further, we perform ablation experiments on each of the model's key components, and the results show that they all contribute to the performance of EnCoSum.

Software clone detection identifies similar or identical code snippets. It has been an active research topic that attracts extensive attention over the last two decades. In recent years, machine learning (ML) based detectors, especially deep learning-based ones, have demonstrated impressive capability on clone detection. It seems that this longstanding problem has already been tamed owing to the advances in ML techniques. In this work, we would like to challenge the robustness of the recent ML-based clone detectors through code semantic-preserving transformations. We first utilize fifteen simple code transformation operators combined with commonly-used heuristics (i.e., Random Search, Genetic Algorithm, and Markov Chain Monte Carlo) to perform equivalent program transformation. Furthermore, we propose a deep reinforcement learning-based sequence generation (DRLSG) strategy to effectively guide the search process of generating clones that could escape from the detection. We then evaluate the ML-based detectors with the pairs of original and generated clones. We realize our method in a framework named CloneGen (stands for Clone Generator). CloneGen In evaluation, we challenge the three state-of-the-art ML-based detectors and four traditional detectors with the code clones after semantic-preserving transformations via the aid of CloneGen. Surprisingly, our experiments show that, despite the notable successes achieved by existing clone detectors, the ML models inside these detectors still cannot distinguish numerous clones produced by the code transformations in CloneGen. In addition, adversarial training of ML-based clone detectors using clones generated by CloneGen can improve their robustness and accuracy. Meanwhile, compared with the commonly-used heuristics, the DRLSG strategy has shown the best effectiveness in generating code clones to decrease the detection accuracy of the ML-based detectors. Our investigation reveals an explicable but always ignored robustness issue of the latest ML-based detectors. Therefore, we call for more attention to the robustness of these new ML-based detectors.

To improve the software debugging efficiency, bug localization techniques have been developed to automatically locate buggy files based on bug reports. Traditional information retrieval-based bug localization cannot deal with the lexical mismatch, thus its performance is limited. In recent years, some deep learning models have been proposed to learn the semantics of bug reports and source files to bridge the lexical gap. However, their accuracy is still limited as building accurate semantic representations of bug reports and source files is very challenging. Recently, relevance matching was proposed to identify whether a document is relevant to a given query by considering both local matching and global matching. In this work, we propose a novel framework DreamLoc, which utilizes a relevance matching model to locate buggy files. Specifically, DreamLoc conducts the local matching by employing an attention-based mechanism to calculate the matching scores between bug report terms and code snippets. It also conducts the global matching by employing a gating mechanism to aggregate results of local matching and obtain the final matching score between a bug report and a source file. Since the local matching considers the relevance between each word and the global matching differentiates the importance of words, DreamLoc can effectively model the characteristics of bug reports and source files. Experimental results on five benchmark datasets show that DreamLoc outperforms five state-of-the-art models. For example, compared with DeepLoc, a recently proposed approach, the evaluation measures Accuracy@10, MAP, and MRR are improved by 6.4%, 7.4%, and 7.2%, respectively.

Code retrieval is a common practice for programmers to reuse existing code snippets in the open-source repositories. Given a user query (i.e., a natural language description), code retrieval aims at searching the most relevant ones from a set of code snippets. The main challenge of effective code retrieval lies in mitigating the semantic gap between natural language descriptions and code snippets. With the ever-increasing amount of available open-source code, recent studies resort to neural networks to learn the semantic matching relationships between the two sources. The statement-level dependency information, which highlights the dependency relations among the program statements during the execution, reflects the structural importance of one statement in the code, which is favorable for accurately capturing the code semantics but has never been explored for the code retrieval task. In this paper, we propose CRaDLe, a novel approach for Code Retrieval based on statement-level semantic Dependency Learning. Specifically, CRaDLe distills code representations through fusing both the dependency and semantic information at the statement level, and then learns a unified vector representation for each code and description pair for modeling the matching relationship. Comprehensive experiments and analysis on real-world datasets show that the proposed approach can accurately retrieve code snippets for a given query and significantly outperform the state-of-the-art approaches on the task.

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named "embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.

API tutorials are important learning resources as they explain how to use certain APIs in a given programming context. An API tutorial can be split into a number of units. Consecutive units that describe a same topic are often called a tutorial fragment. We consider the API explained by a tutorial fragment as an API tag. Generating API tags for a tutorial fragment can help understand, navigate, and retrieve the fragment. Existing approaches often do not perform well on API tag generation due to high manual effort and low accuracy. Like API tutorials, Stack Overflow (SO) is also an important learning resource that provides the explanations of APIs. Thus, SO posts also contain API tags. Besides, API tags of SO posts are abundant and can be extracted easily. In this paper, we propose a novel approach ATTACK (stands for A PI T ag for T utorial frA gments using C rowd K nowledge), which can automatically generate API tags for tutorial fragments from SO posts. ATTACK first constructs (Q&Apair,tagset) pairs by extracting API tags of SO posts. Then, it trains a deep neural network with the attention mechanism to learn the semantic relatedness between Q&A pairs and the associated API tags, taking into consideration both textual descriptions and code in a Q&A pair. Finally, the trained model is used to generate API tags for tutorial fragments. We evaluate ATTACK on public Java and Android datasets containing 43,132 (Q&Apair,tagset) pairs. Experimental results show that ATTACK is effective and outperforms the state-of-the-art approaches in terms of F-Measure. Our user study further confirms the effectiveness of ATTACK in generating API tags for tutorial fragments. We also apply ATTACK to document linking and the results confirm the usefulness of API tags generated by ATTACK.

In software architecture design, we explore design alternatives and make decisions about adoption or rejection of a design from a web of complex and often uncertain information. Different architectural design decisions may lead to systems that satisfy the same set of functional requirements but differ in certain quality attributes. In this paper, we propose a Bayesian Network based approach to rational architectural design. Our Bayesian Network helps software architects record and make design decisions. We can perform both qualitative and quantitative analysis over the Bayesian Network to understand how the design decisions influence system quality attributes, and to reason about rational design decisions. We use the KWIC (Key Word In Context) example to illustrate the principles of our approach. © World Scientific Publishing Company.

Software reuse focused on product lines has emerged as one of the promising ways to increase software productivity and quality. XVCL (XML-based Variant Configuration Language) is a variability mechanism that we developed for handling variants in software product lines. We apply XVCL to develop product line assets (including the domain model, product line architecture and generic components) as a set of x-frames that are capable of accommodating both commonality and variability in a domain. Specific systems, members of a product line, can be constructed by adapting and composing x-frames. In this paper, we illustrate our approach using examples from our product line project on the Computer Aided Dispatch¿(CAD) domain. © 2004 Elsevier B.V. All rights reserved.

In recent years, software product lines have emerged as a promising approach to improve software development productivity in IT industry. In the product line approach, we identify both commonalities and variabilities in a domain, and build generic assets for an organization. Feature diagrams are often used to model common and variant product line requirements and can be considered part of the organizational assets. Despite their importance, quality attributes (or non-functional requirements, NFRs) such as performance and security have not been sufficiently addressed in product line development. A feature diagram alone does not tell us how to select a configuration of variants . to achieve desired quality attributes of a product line member. There is a lack of an explicit model that can represent the impact of variants on quality attributes. In this paper, we propose a Bayesian Belief Network (BBN) based approach to quality prediction and assessment for a software product line. A BBN represents domain experts' knowledge and experiences accumulated from the development of similar projects. It helps us capture the impact of variants on quality attributes, and helps us predict and assess the quality of a product line member by performing quantitative analysis over it. For developing specific systems, members of a product line, we reuse the expertise captured by a BBN instead of working from scratch. We use examples from the Computer Aided Dispatch (CAD) product line project to illustrate our approach. © Springer-Verlag Berlin Heidelberg 2003.