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With the emergence of mixed source software, the existing quality models are not able to better assess the community quality and autonomy controllability of mixed source software. To fill this gap, we propose a hierarchical model in this paper for quality assessment of mixed source software. In our model, the new attributes are proposed to meet the quality requirements of mixed source software based on the ISO/IEC 25010 standards, and a set of metrics are designed for the new attributes. The model evaluates the quality of mixed source software through quality attributes that have been quantified by the metrics. Applying our quality model to some mixed source software and comparing the model results with the actual situation, we verify whether our proposed two quality attributes, community intensity and autonomy controllability, can effectively assess the quality of mixed source software. The results of the experiments show that our model is indeed effective in assessing the quality of mixed source software. An important feature of our model is that the model has good flexibility, and the set of quality attributes and metrics can be adjusted freely, which provides a flexible and feasible way for various software quality assessment requirements.

The quality attributes of software architecture (SA) determine whether SA can be easily understood, tested, modified and so on, so quality-driven architecture evolution is important for keeping the viability and competitiveness. SA evolution is a process, and it contains multiple steps, such as SA quality measurement, SA modification, code co-evolution and so on. In order to guarantee that the software can be continuously improved and iteratively evolved in the future, we need to focus on all steps. However, most existing approaches only focus on one aspect, so they did not pay attention to how to finish the evolution process. In this paper, we propose a quality-driven iterative evolution approach for SA. This approach focuses on the whole process. In the first step, we use a quantitative approach to measure the architecture quality. Then, we construct the conflict graph to detect conflicts between evolution requirements to generate the final evolution scheme. In the third step, we modify architecture based on the evolution scheme. Finally, we co-evolve file dependency graph (FDG) based on the modified architecture. By focusing on the above steps, our approach can support a complete quality-driven architecture evolution process and obtain the maximum benefit in terms of the combined SA quality. We conduct our experiments with four open source projects, the experimental results indicate that our approach can improve SA quality, and our approach can effectively co-evolve the FDG to lay the foundation for the next evolution.

More than 75% of recent Java projects include some form of concurrent programming. Due to complex interactions between multi-threads, concurrent programs are often harder to understand and test than single threaded programs. To facilitate understanding and testing of concurrent programs, we developed a new profiling method called TCP (Thread Control Profiling). Outputs of TCP presents frequencies of control dependence, which includes thread creation, thread synchronization, interruption, and so on, of the executed thread. TCP first performs static analysis of detailed concurrency syntax and semantics of Java to construct the profiling graph model TCDG (Thread Control Dependence Graph). TCDG is then used for instrumentation and for generating profiles. We have evaluated TCP using a case study and a few experiments. The case study shows that TCP method can effectively prioritize test cases for testing concurrent programs. One experiment shows that outputs from TCP facilitate developers’ understanding of concurrent code. Other experiments evaluate various possible overheads introduced by the TCP method. Results show that TCP can provide rich and useful information with reasonable costs.

Comments are beneficial for developers to understand and maintain the code in software development life cycle. Well-commented code can generally help developers to resolve issues efficiently. Due to the complexity of code implementation, code comments may be generated to represent different types of information. And it is hard to keep all the code well-commented in real-world projects. In this case, it is meaningful to investigate how the different types of comments impact the resolution of issues. Then we can maintain the code comments purposefully, and we can also provide some suggestions for the comment generation techniques. To analyze the efforts of different comments on issue resolution, we classify code comments into two categories, i.e., functionality-aspect and non-functionality-aspect comments. In this paper, we analyze the effects of 53k pieces of code comments on the issues from 10 open-source projects within a period of 24 months. The results show that the majority of code comments are used to represent the functionality, e.g., the summary and purpose of code. Nevertheless, the other non-functionality-aspect comments have much stronger correlation with the resolution of software issues. For the resolved patches, the non-functionality-aspect comments are more frequently to be updated or added than the functionality-aspect comments. These findings confirm the important role of non-functionality-aspect comments during issue resolution, although their proportion is far less than that of functionality-aspect comments.

Code smells are used to describe the bad structures in the source code, which could hinder software maintainability, understandability and changeability. Nowadays, scholars mainly focus on the impact of smell on textual change-proneness. However, in comparison to textual changes, structural changes could better reveal the change nature. In practice, not all code change types are equally important in terms of change risk severity levels, and software developers are more interested in particular changes relevant to their current tasks. Therefore, we investigate the relationship between smells and fine-grained structural change-proneness to solve these issues. Our experiment was conducted on 11 typical open source projects. We first employed Fishers exact test and Mann–Whitney test to explore whether smelly files (affected by at least one smell type) had higher structural change-proneness than other files, and whether files with more smell instances are more likely to undergo structural changes, respectively. Multivariate logistic regression model was built to study the relation between each kind of smell and change-proneness with respect to five change categories. Our results showed that: (1) in most cases, smelly files were more prone to structural changes and files with more smell instances tend to undergo higher structural changes; (2) quite a few smell types were related to structural change-proneness, particularly, Refused Parent Bequest (RPB), Message Chains (MCH), Divergent Change (DIVC), Feature Envy (FE) and Shotgun Surgery (SS) increased structural changes for some change categories. However, when controlling the file size Lines of Code (LOC), significant change-proneness of some smells disappeared or the magnitude of significance decreased more or less.

Manual program repair is notoriously tedious, error-prone, and costly, especially for the modern large-scale projects. Automated program repair can automatically find program patches without much human intervention, greatly reducing the burden of developers as well as accelerating software delivery. Therefore, much research effort has been dedicated to design powerful program repair techniques. To date, although various program repair techniques have been proposed, to our knowledge, there lacks extensive study on the impacts of repair techniques, subject programs, and test suites on the repair effectiveness and efficiency. In this paper, we perform such an extensive study on repairing 180 seeded and real faults from 17 small to large sized programs. We study the impacts of five representative automated program repair techniques, including GenProg, RSRepair, Brute-force-based technique, AE and Kali, on the repair results. We further investigate the impacts of different subject programs and test suites on effectiveness and efficiency of program repair techniques. Our study demonstrates a number of interesting findings: Brute-force-based technique generates the maximum number of patches but is also the most costly technique, while Kali is the most efficient and has medium effectiveness among the studied techniques; techniques that work well with small programs become too costly or ineffective when applied to large sized programs; since tool-reported patches may overfit the selected test cases, we calculate the false positive rates and find that the influence of failed test cases is much larger than that of passed test cases; finally, surprisingly, all the studied techniques except RSRepair can find more than 80% of successful patches within the first 50% of search space.

3Directory structure contains a wealth of software design information; it is used to transfer thoughts of architects to developers. Information extracted from directory paths should play an important role in architecture recovery techniques, but it has been proved that modules or components directly represented by directories are not accurate due to the inconsistency between stages of development and design. To make better use of information extracted from directories, we propose a directory-based dependency processing technique to utilize the information of directories in the process of structure-based architecture recovery. The technique groups the selected inter-coupling files and intra-coupling files in the same directory into a submodule and generates submodule-level dependency graph based on file-level dependency graph. We apply both manual and automatic architecture recovery techniques on submodule-level dependency graph, and the results show that our technique can greatly improve the efficiency and effectiveness of manual and automatic architecture recovery techniques; the technique can also make other structure-based recovery techniques easily scalable to large-sized projects.

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.

Natural Language Processing (NLP) library is widely used while analyzing software documents. The numerous toolkits result in a problem on NLP library selection. The selection of NLP library in current work commonly misses some objective reasons, which may pose threats to validity. And it is also not clear that whether the existing guideline on selection still works for the latest versions. In this work, we propose a solution for NLP library selection when the effectiveness is unknown. We use the NLP libraries together in a combined method. Our combined method can utilize the strengths of different NLP libraries to obtain accurate results. The combination is conducted through two steps, i.e., document-level selection of NLP library and sentence-level overwriting. In document-level selection of primary library, the results are obtained from the library that has the highest overall accuracy. Through sentence-level overwriting, the possible fine-gained improvements from other libraries are extracted to overwrite the outputs of primary library. We evaluate the combined method with 4 widely used NLP libraries and 200 documents from 3 different sources. The results show that the combined method can generally outperform all the studied NLP libraries in terms of accuracy. The finding means that our combined method can be used instead of individual NLP library for more effective results.

Design patterns (DPs) are widely accepted as solutions to recurring problems in software design. While numerous approaches and tools have been proposed for DP detection over the years, the neglect of language-specific mechanism that underlies the implementation idioms of DPs leads to false or missing DP instances since language-specific features are not captured and similar characteristics are not distinguished. However, there is still a lack of research that emphasizes the idiomatic implementation in the context of a specific language. A vital challenge is the representation of software systems and language mechanism. In this work, we propose a practical approach for DP detection from source code, which exploits idiomatic implementation in the context of Java language. DPs are formally defined under the blueprint of the layered knowledge graph (LKG) that models both language-independent concepts of DPs and Java language mechanism. Based on static analysis and inference techniques, the approach enables flexible search strategies integrating structural, behavioral and semantic aspects of DPs for the detection. Concerning emerging patterns and pattern variants, the core methodology supports pluggable pattern templates. A prototype implementation has been evaluated on five open source software systems and compared with three other approaches. The evaluation results show that the proposed approach improves the accuracy with higher precision (85.7%) and recall (93.8%). The runtime performance also supports its practical applicability.

During software evolution, change detection plays an important role in software maintenance. For example, according to the changes, developers need to verify whether the evolved contents are consistent with the evolution plan, and making a regression testing plan. Right now, the text-based method and the AST-based matching method are widely used to detect changes. However, the text-based method cannot identify updating and renaming, so the accuracy of its detecting result is not high enough; the AST-based matching method only reflects the changes between files, so its detecting result is incomplete for understanding software evolution from an overall perspective. In order to solve the above problems, we propose a software multiple-level change detection method based on two-step MPAT (multilevel program analysis tree) matching, which detects changes between two programs from multiple levels to improve accuracy and proposes comprehensive change information. Our approach consists of three steps. Firstly, we construct MPAT for each program. Secondly, we implement the two-step matching algorithm to detect changes based on the two MPATs. Thirdly, these changes are classified and clustered. Finally, we develop ChangeAnalyzer to implement our approach, and conduct our experiments on six projects to evaluate the accuracy, performance and usefulness of ChangeAnalyzer.

Context: Syntax is fundamental to any programming language: syntax defines valid programs. In the 1970s, computer scientists rigorously and empirically studied programming languages to guide and inform language design. Since then, language design has been artistic, driven by the aesthetic concerns and intuitions of language architects. Despite recent studies on small sets of selected language features, we lack a comprehensive, quantitative, empirical analysis of how modern, real-world source code exercises the syntax of its programming language. Objective: This study aims to understand how programming language syntax is employed in actual development and explore their potential applications based on the results of syntax usage analysis. Method: We present our results on the first such study on Java, a modern, mature, and widely-used programming language. Our corpus contains over 5000 open-source Java projects, totalling 150 million source lines of code (SLoC). We study both independent (i.e. applications of a single syntax rule) and dependent (i.e. applications of multiple syntax rules) rule usage, and quantify their impact over time and project size. Results: Our study provides detailed quantitative information and yields insight, particularly (i) confirming the conventional wisdom that the usage of syntax rules is Zipfian; (ii) showing that the adoption of new rules and their impact on the usage of pre-existing rules vary significantly over time; and (iii) showing that rule usage is highly contextual. Conclusions: Our findings suggest potential applications across language design, code suggestion and completion, automatic syntactic sugaring, and language restriction.

ContextApplication Programming Interfaces (APIs) facilitate the use of programming languages. They define sets of rules and specifications for software programs to interact with. The design of language API is usually artistic, driven by aesthetic concerns and the intuitions of language architects. Despite recent studies on limited scope of API usage, there is a lack of comprehensive, quantitative analyses that explore and seek to understand how real-world source code uses language APIs. ObjectiveThis study aims to understand how APIs are employed in practical development and explore their potential applications based on the results of API usage analysis. MethodWe conduct a large-scale, comprehensive, empirical analysis of the actual usage of APIs on Java, a modern, mature, and widely-used programming language. Our corpus contains over 5000 open-source Java projects, totaling 150 million source lines of code (SLoC). We study the usage of both core (official) API library and third-party (unofficial) API libraries. We resolve project dependencies automatically, generate accurate resolved abstract syntax trees (ASTs), capture used API entities from over 1.5 million ASTs, and measure the usage based on our defined metrics: frequency, popularity and coverage. ResultsOur study provides detailed quantitative information and yield insight, particularly, (1) confirms the conventional wisdom that the usage of APIs obeys Zipf distribution; (2) demonstrates that core API is not fully used (many classes, methods and fields have never been used); (3) discovers that deprecated API entities (in which some were deprecated long ago) are still widely used; (4) evaluates that the use of current compact profiles is under-utilized; (5) identifies API library coldspots and hotspots. ConclusionsOur findings are suggestive of potential applications across language API design, optimization and restriction, API education, library recommendation and compact profile construction.

Cyclomatic complexity (CC) is often used as a factor to evaluate the quality of source code. Many researchers have studied the relationships between CC and LOC, between CC and basic testing paths, and between CC and code maintainability etc. However, few researchers studied how software evolution affects CC. In this paper, we propose a methodology based on source code change analysis and develop a supporting tool, called CCEvaluator, to evaluate CC variation during software evolution. By empirical studies on six pieces of typical open source projects, a series of interesting findings including six commonness and five differences have been obtained. To explain why these commonness and differences are produced during software evolution, code change information among successive versions are captured and used in this paper.

We present a comparative study of different static CIA techniques, Columbus, ROSE, and IRC2M.IRC2M and ROSE achieve relatively better accuracy compared to Columbus.Combination of different CIA techniques can obviously improve the accuracy over their individual one.Combining all three CIA techniques obtain a similar precision and recall as combining ROSE with IRC2M. Software Change Impact Analysis (CIA) is an essential technique to identify the potential effects caused by software changes during software maintenance and evolution. A rich body of CIA techniques, especially static CIA techniques, have continuously emerged in recent years such as structural static analysis, textual analysis, and historical analysis. However, there were only a few works focusing on comparison of static CIA techniques. This article attempts to bridge this gap by presenting a comparative study of three class-level static CIA techniques, i.e., Columbus, ROSE, and IRC2M. We compare them based on a CIA comparative framework and conduct an empirical study to evaluate these three CIA techniques and their combinations based on five real-world programs. The empirical results show that: (1) IRC2M and ROSE achieve relatively better precision, recall and F-measure compared to Columbus; (2) combination of any two CIA techniques can improve the precision and recall over their individual one; moreover, combining ROSE with IRC2M produces the best impact results; and (3) combining all three CIA techniques obtain a similar precision and recall as combining ROSE with IRC2M.

Context Mining software repositories has emerged as a research direction over the past decade, achieving substantial success in both research and practice to support various software maintenance tasks. Software repositories include bug repository, communication archives, source control repository, etc. When using these repositories to support software maintenance, inclusion of irrelevant information in each repository can lead to decreased effectiveness or even wrong results. Objective This article aims at selecting the relevant information from each of the repositories to improve effectiveness of software maintenance tasks. Method For a maintenance task at hand, maintainers need to implement the maintenance request on the current system. In this article, we propose an approach, MSR4SM, to extract the relevant information from each software repository based on the maintenance request and the current system. That is, if the information in a software repository is relevant to either the maintenance request or the current system, this information should be included to perform the current maintenance task. MSR4SM uses the topic model to extract the topics from these software repositories. Then, relevant information in each software repository is extracted based on the topics. Results MSR4SM is evaluated for two software maintenance tasks, feature location and change impact analysis, which are based on four subject systems, namely jEdit, ArgoUML, Rhino and KOffice. The empirical results show that the effectiveness of traditional software repositories based maintenance tasks can be greatly improved by MSR4SM. Conclusions There is a lot of irrelevant information in software repositories. Before we use them to implement a maintenance task at hand, we need to preprocess them. Then, the effectiveness of the software maintenance tasks can be improved.

Pointer analysis is a key static analysis during compilation. Several client analyses and transformations rely on precise pointer information to optimize programs. Therefore, it is paramount to improve the efficiency of pointer analysis. A critical piece of an inclusion-based pointer analysis is online cycle detection. The efficiency of pointer analysis is significantly influenced by the efficacy of detecting cycles. Existing approaches perform poorly when they guess cycle formation in the constraint graph. Thus, the number of false cycle-detection triggers of the state-of-the-art methods is considerably high over 99% on Standard Performance Evaluation Corporation SPEC benchmarks. In this paper, we propose bootstrapping as a way to improve cycle detection predictability of pointer analysis. The main idea is to run a sequence of increasingly precise analyses to feed into the next more precise analysis to improve the efficiency of the latter analysis. In this process, we develop a new notion of pointer equivalence called constraint equivalence. Using Steensgaard's fast unification algorithm as the bootstrap, we devise a new cycle detection method for Andersen's inclusion-based analysis. We measure the effectiveness of our approach using a suite of programs including SPEC 2000 benchmarks and two open-source programs, and find that our method can reduce the number of false cycle detections by almost 22× compared with a state-of-the-art method. This leads to an overall analysis time improvement of 18% on an average. Copyright © 2015 John Wiley & Sons, Ltd.

Composite services implement value-added functionality by composing service components of various granularities. Trust is an important criterion to judge whether a composite service can behave as expected. There is a great need for a flexible trust evaluation method for composite services. In this paper, a data dependency-based trust evaluation approach for composite services is proposed. Firstly, we derive define-use pairs of variables to identify data dependency between service components in BPEL processes modelled by an extensible BPEL flow graph (XBFG); then, dependency links including both direct and indirect data dependencies are used to evaluate the trust of these service components; furthermore, on the basis of BPEL structure and XBFG, reduction rules are proposed to evaluate the global trust of BPEL processes. Experimental results demonstrate that the proposed approach is effective for the trust evaluation of BPEL composite services and stable with the growing number of service components in BPEL.

Software change is a fundamental ingredient of software maintenance and evolution. Effectively supporting software modification is essential to provide a reliable high-quality evolution of software systems, as even a slight change may cause some unpredictable and undesirable effects on other parts of the software. To address this issue, this work used change impact analysis (CIA) to guide software modification. CIA can be used to help make correct decision on the change proposal, that is changeability assessment, and to implement effective changes for a change proposal. In this article, we conducted an empirical study on three Java open-source systems to show how CIA can be used during software modification. The results indicate that: (1) assessing changeability of a change proposal based on the impact results of the CIA is not accurate from the precision perspective; (2) the proposed impactness metric is an effective indicator of changeability assessment for the change proposal; and (3) CIA can make the change implementation process more efficient and easier.

Highlights? Propose a new combined CIA approach. ? Impact factor is used to compute final imapct set. ? Experiments show that our CIA approach improves the precision. BackgroundSoftware Change Impact Analysis (CIA) is an essential technique in software engineering to identifying the potential influences of a change, or determining change entities to accomplish such a change. The results derived, in many cases, ambiguous for the software maintainers, introduces the problem of unclear starting point of these impacted entities. ObjectiveIn an attempt to address this issue, this work proposes a novel approach for cross-level CIA, producing a ranked list of potentially impacted methods derived from class-level changes. Moreover, the approach of ranking the impact results is expected to be effective for maintainers to distinguish the probability of the impacted methods to be false-positives. Such results provide an eclectic approach for CIA. MethodThe approach, FCA-CIA, uses formal concept analysis (FCA) to produce an intermediate representation of the program based on the static analysis of the source code. The representation is called Lattice of Class and Method Dependence (LoCMD). FCA-CIA takes the changed classes in the change set as a whole, and determines the reachable set from the changed classes on the LoCMD. Based on the hierarchical property of the LoCMD, the impacted methods are ranked according to the impact factor metric which corresponds to the priority of these methods to be inspected. ResultEmpirical evaluations on four real-world software projects demonstrate the effectiveness of the impact factor metric and the FCA-CIA technique. The result shows the predicted impacted methods with higher impact factor values are more likely to be affected by the changes. Our study also shows that the FCA-CIA technique generates more accurate impact set than the JRipples and ICP coupling based CIA technique.

Software change impact analysis (CIA) is a key technique to identify the potential effects caused by software changes. Given the proposed changes, most commonly used CIA techniques apply reachability analysis on the graphical representation of the software to identify their effects. They often compute a large set of potentially impacted elements with many false-positives, which will waste resource in later analysis. In addition, these techniques do not consider the interference among the proposed changes, which does exist in practice. Faced with these problems, this paper proposed a new call graph-based CIA technique, which takes the interference among multiple proposed changes into account to improve the precision of the impact results. Our proposed CIA is inspired by a natural phenomenon ''water wave propagation''. The CIA process is similar to the process of water wave propagation. First we identify the ''core'' (a special set of methods) generated by the proposed changes. Then we compute the ripple effects through propagation analysis on this core. Empirical evaluations on two real-world software projects demonstrate the effectiveness of our CIA approach. The results show that our CIA technique can predict better impact results when more changes are known, and provide an eclectic way for practical use. Moreover, it can effectively remove the false-positives at the cost of missing a few false-negatives when compared to traditional (call graph based) CIA techniques.

Software change impact analysis (CIA) is a key technique for identifying unpredicted and potential effects caused by changes made to software. Different changes have different ripple effects to other parts in the program, even some changes do not affect other entities in spite of some dependencies existing between these entities and the modified one. This induces imprecision if such a factor is neglected. This article proposes a static CIA technique which considers the impact rules of different change types to predict the change effects. Input of our CIA includes changed classes, class methods and class fields, and the output is composed of potentially affected classes, class methods, and class fields. Precision improvement of the CIA technique relies on three aspects: change types of a modified entity, dependencies between the modified entity and other entities, and a precise initial impact set (IIS), on which the final impact set (FIS) is computed. Experimental case studies demonstrate the effectiveness of our technique, and present its potential applications in software maintenance.

SOA (Service-oriented Architecture) is becoming the major architecture for building complex Web service systems. In SOA, the lifecycle of Web services consists of four stages: service publication, service discovery, service binding and service execution. Faults may occur in any stage and cause a failure of service. The reliability of services depends on not only the faults of the remote services itself, but also the faults that may occur in any SOA stage. Designing an effective and accurate reliability prediction model for Web services has become an important and necessary task. This study first proposes a staged reliability model for atomic services, which structures the reliability estimation based on the faults that may occur at each stage. Then, the reliability of composite services or service systems can be estimated based on this staged model. An experiment is conducted to study the influence of failures at each stage on the reliability of atomic services and a case study is designed to demonstrate the reliability of composite services using our staged model. In addition, the accuracy of our approach is investigated by comparing it against CBRM (component-based reliability model). Sensitivity analysis is also performed to identify critical service. Experimental results show that our staged model can obtain higher reliability prediction accuracy and is more suitable for Web services than traditional model.

During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages. Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend. Empirical studies on four real-world software projects demonstrate the effectiveness of our approach. The results show that the effectiveness of our approach is better than Latent Semantic Indexing- (LSI-) and Probabilistic Latent Semantic Analysis- (PLSA-) based clustering approaches. In addition, we find that the topic that labels each cluster is useful for program comprehension.

Software change impact analysis (CIA) is a key technique to identify unexpected and potential effects caused by software changes. Given a changed entity, most of current CIA techniques compute the change effect composed of some potentially impacted entities. The generated results are often of no help to the maintainers in starting the analysis of impacted entities. In this article, we combine concept lattice with call graph together to obtain a ranked list of potentially impacted methods from the proposed changed methods and/or classes. These impacted methods are ranked based on the hierarchical feature of concept lattice, represented by an impact factor, which can then be used to prioritize these methods to be inspected. Case studies based on four real-world programs show that our approach can improve the precision of the impact result without severely decreasing its recall, when compared with results from either concept lattice or call graph used independently. In addition, the predicted impacted methods with higher impact factor values are also shown to have higher probability to be affected by the changes. Our study also shows that our approach is better than the JRipples CIA approach in removing the false-positives, but at the cost of losing more false-negatives and much more time overhead.

As an important technique in dynamic program analysis, path profiling collects the execution frequency of different paths, and has been widely used in a variety of areas. However, existing intra-procedural profiling techniques cannot effectively deal with loops, i.e., they are limited in either working with acyclic paths, or with a small number of loop iteration. This paper presents a new profiling technique called PAP (Profiling All Paths), which can profile all finite-length paths within a procedure. PAP consists of two basic phases, the probe instrumentation phase which assigns a unique pathid to each path, and the backwalk phase which uses the pathids to determine the corresponding executed paths. Furthermore, breakpoints are introduced to store the probe value which may overflow during long executions, and the number of probes is reduced based on the integration of PAP with an existing profiling technique. From our case study and experiments, PAP is found to be effective and efficient in profiling both cyclic and acyclic paths.

Highlights? We propose a flow graph to model BPEL-based composite service. ? XBFG path is introduced and analyzed for test case selection. ? A new classification for service modification is discussed. ? Five different versions of a running example are explored. Services are highly reusable, flexible and loosely coupled components whose changes make the evolution and maintenance of composite services more complex. The changes of composite service mainly cover three types, i.e., the processes, bindings, and interfaces. In this article, an approach is proposed to select test cases for regression testing of different versions of BPEL (business process execution language) composite service where these changes are involved. The approach identifies the changes by performing control flow analysis and comparing the paths in a new version of composite service with those in the old one using a kind of eXtensible BPEL flow graph (XBFG). Message sequence is appended to XBFG path so that XBFG can fully describe the behavior of composite service. The binding and predicate constraint information added in different XBFG elements can be used for path selection and even for test case generation. Both theoretic analysis and case study show that the proposed approach is effective.

SUMMARY Software change impact analysis (CIA) is a technique for identifying the effects of a change, or estimating what needs to be modified to accomplish a change. Since the 1980s, there have been many investigations on CIA, especially for code-based CIA techniques. However, there have been very few surveys on this topic. This article tries to fill this gap. And 30 papers that provide empirical evaluation on 23 code-based CIA techniques are identified. Then, data was synthesized against four research questions. The study presents a comparative framework including seven properties, which characterize the CIA techniques, and identifies key applications of CIA techniques in software maintenance. In addition, the need for further research is also presented in the following areas: evaluating existing CIA techniques and proposing new CIA techniques under the proposed framework, developing more mature tools to support CIA, comparing current CIA techniques empirically with unified metrics and common benchmarks, and applying the CIA more extensively and effectively in the software maintenance phase. Copyright © 2012 John Wiley & Sons, Ltd.

The Web Services Choreography Description Language (WS-CDL) is a specification developed by the W3C and can be viewed as a blueprint for the development of end-point services. Consequently, it is worth providing a systematic approach for its modeling, analysis and verification. The Unified Modeling Language (UML) is an industry standard for modeling. Applying UML to model WS-CDL is obviously a promising solution to bring together academics and practitioners through a unique standard language. In this paper, we propose to use different UML diagrams to model WS-CDL. UML Component Diagram is used to model the underlying structure of WS-CDL. UML Sequence Diagram is utilized to model the activities in WS-CDL. UML State Machine Diagram is utilized to model the behaviors of each role participating in a WS-CDL specification. We then enrich the UML State Machine Diagram with data by the use of UML Class Diagram. Given the UML specification of WS-CDL, we then provide a systematic way of formally analyzing and verifying WS-CDL against desired properties. Some experiments show that our approach can verify structural, behavioral and data properties in a middle-scale data-enriched WS-CDL specification.

In web service times, the techniques for composing services are based on service reuse and automatic integration. A new web service will be generated by composing some existing web services. These web services cooperate with each other to provide a new more complex function. It is necessary and very important to test the interaction behavior between any two web services during composition. In this paper, a kind of enhanced hierarchical color petri-net (or EH-CPN) is introduced to generate test cases for testing the interaction, where EH-CPN is transformed from OWL-S document, and both control flow and data flow information in EH-CPN are analyzed and used to generate an executable test sequence, and further test cases are created by combining the test sequence and test data in an XML file.

Software architecture specifications are used for many different purposes, such as documenting architectural decisions, predicting architectural qualities before the system is implemented, and guiding the design and coding process. In these contexts, assessing the architectural model as early as possible becomes a relevant challenge. Various analysis techniques have been proposed for testing, model checking, and evaluating performance based on architectural models. Among them, model checking is an exhaustive and automatic verification technique, used to verify whether an architectural specification conforms to expected properties. While model checking is being extensively applied to software architectures, little work has been done to comprehensively enumerate and classify these different techniques. The goal of this paper is to investigate the state-of-the-art in model checking software architectures. For this purpose, we first define the main activities in a model checking software architecture process. Then, we define a classification and comparison framework and compare model checking software architecture techniques according to it.

Property Sequence Chart (PSC) is a novel scenario-based notation, which has been recently proposed to represent temporal properties of concurrent systems. This language balances expressive power and simplicity of use. However, the current version of PSC just represents the order of events and lacks the ability to express timing properties. In real-time systems, it is well known that these timing requirements are very important and need to be specified clearly. Thus, in this paper, we define timed PSC (TPSC) and give the semantics of TPSC in terms of Timed Buchi Automaton (TBA). Then, we measure the expressive power of TPSC based on the recently proposed real-time specification patterns. Finally, we illustrate the use of TPSC in the context of a web service application which requires timing requirements.

Component dependence analysis is a useful technique that has many applications in software engineering activities including software understanding, testing, debugging, maintenance, and evolution. In this paper, we propose a matrix-based representation to analyze dependences in component-based system (CBSs). By further investigating the application of matrix in analyzing dependences of component-based system, we find that it has many applications in software quality assurance. To make it possible, we first identify all types of dependences in a CBS, and then present a dependence-based representation called the component dependence graph (CDG) and the dependence matrix (DM) to explicitly represent these dependences in a CBS. Based on the CDG and DM, we build a mathematical basis for managing and analyzing dependences in a CBS. Finally, we discuss some important applications of the dependence analysis technique in component-based software quality assurance, such as understanding, testing, maintenance, and evolution etc.

Program slicing can be effectively used to debug, test, analyze, understand and maintain object-oriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in a stepwise way, from package level, to class level, method level, and finally up to statement level. The stepwise slicing algorithm and the related graph reachability algorithms are presented, the architecture of the Java program Analyzing TOol (JATO) based on hierarchical slicing model is provided, the applications and a small case study are also discussed.

Traditional information-flow analysis is mainly based on data-flow and control-flow equations. In object-oriented programs, because of the mechanisms such as encapsulation, inheritance, and polymorphism, information-flow analysis becomes quite complex and therefore it is not enough to analyze object-oriented information-flow using traditional techniques. Some new techniques are required in order to efficiently analyze the information-flow between basic components (such as statements, methods, classes or packages) in object-oriented programs. Based on object-oriented program slicing techniques, we discuss how to compute the amount of information-flow, the width of information-flow, the correlation coefficient between basic components as well as degree of coupling of a basic component can be computed. We also discuss some applications of the information-flow.

Software change impact analysis (CIA) is a key technique to identify the potential effects caused by software changes. In this paper, a new graph mining based CIA technique is proposed, which takes the interference among multiple proposed changes into account to improve the precision of the impact results. Empirical evaluations on two real-world software projects demonstrate the effectiveness of our CIA technique.

Software Change Impact Analysis (CIA) is an essential technique to identify the unpredicted and potential effects caused by software changes. A rich body of different CIA techniques, especially static CIA techniques, have continuously emerged in recent years. However, it is difficult for researchers or practitioners to decide which technique is most appropriate for their needs, or which CIA technique is more effective. Unfortunately, there was only a few work on the comparison of the CIA techniques. This paper presents a comparison study of different types of popular static CIA approaches, i.e., structural static analysis, textual analysis, and historical analysis. For each kind of static CIA approach, we introduce a representative technique, that is FCA -- CIA, ROSE, and IRC2M, respectively. Finally, some empirical studies are conducted on three real-world programs to compare the accuracy of these CIA techniques based on the precision and recall metrics. The results show that the accuracy of these three CIA techniques is different, and FCA - CIA has the best precision while the IRC2M has the best recall.

Software needs to be maintained and changed to cope with new requirement, existing faults and change requests as software evolves. One particular issue in software maintenance is how to deal with a change proposal before change implementation? Changes to software often cause unexpected ripple effects. To avoid this and alleviate the risk of performing undesirable changes, some predictive measurement should be conducted and a change scheme of the change proposal should be presented. This research intends to provide a unified framework for change analysis, which includes dependencies extraction, change impact analysis, changeability assessment, etc. We expect that our change analysis framework will contribute directly to the improvement of the accuracy of these predictive measures before change implementation, and thus provide more accurate change analysis results for software maintainers, improve quality of software evolution and reduce the software maintenance effort and cost.

A common problem in object-oriented software integration testing is to determine the order in which classes are integrated and tested. This paper proposes an effective approach to automatically generate a (near) optimal test order from Java source code. This approach includes three aspects: constructing an extended test dependency graph to represent classes and inter-class dependencies, measuring inter-class coupling information to estimate stub complexity, and providing a fast heuristic algorithm to break cycles.

Path profiling records the frequency of each path in an executed routine. To accomplish profiling, probes are instrumented in a program and executed as the program runs. So the number of probes has important influences on the efficiency of a profiling technique. To profile only a subset of paths, existing techniques try to improve the profiling efficiency by reducing probes, optimizing path encoding, and so on. However, they mainly lack accuracy, waste time on running uninterested paths, and only deal with acyclic paths. In this paper, a novel technique called PSP (Profiling Selective Paths) has been introduced to profile selective paths, which can handle selection for both acyclic and cyclic paths, and increase the execution efficiency by early termination on uninterested paths. PSP is implemented in two ways, PSP1 and PSP2. Theoretical comparison and experimental evaluation indicate that PSP1 and PSP2 perform differently but both effectively.

Software change impact analysis (CIA) is a key technique to identify unpredicted and potential effects caused by software changes. In this paper, we propose a new CIA technique based on a compact and effective representation for object oriented programs, called lattice of class and method dependence (LoCMD). This novel representation can effectively capture the dependences between classes and methods. Based on the LoCMD, our CIA technique calculates a ranked list of potential impacted methods according to a metric, impact factor, which corresponds to the priority of these methods to be inspected. Initial case study validates the reasonability of our two assumptions, and demonstrates the effectiveness of our technique.

Software change impact analysis (CIA) is a key technique for identifying unpredicted and potential effects caused by changes made to software. Different change types often have different impact mechanisms, even some changes do not impact other entities in programs in spite of some dependences existed between these entities and the modified entity. In this paper, we propose a static CIA technique, which considers different impact mechanisms and rules of different change types, to calculate the impact sets. Precision improvement of the impact sets relies on 3 aspects: change types of a modified entity, dependences between the modified entity and other entities, and the intuition that to win at the start -- if the initial impact set is estimated more accurately, then the final impact set depending on this initial impact set will be more precise. Experimental case study demonstrates the effectiveness of our technique, and its potential applications in software maintenance.

Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Due to the autonomous attribute of each basic service, validation of composite services must be extended from design time to run-time. In this paper, we describe a novel tool chain called WS-PSC Monitor to monitor temporal and timing properties in composite service based on graphical specification property sequence chart and timed property sequence chart. The tool chain provides a completely graphical front-end which can make software designers do not have to deal with any particular textual and logical formalism.

Hierarchical program slicing which is stratified calculated based on the inherent level characteristics of object- oriented programs is an effective technique for narrowing the errors to the relevant parts of a program and improving the efficiency when debugging. Given a hierarchical slicing criterion, the hierarchical slice contains only those packages, classes, methods and statements that actually affect the variables in the slicing criterion. This paper proposes a method step by step to narrow the scope of object-oriented program based on hierarchical slicing. It is well acknowledge that software testing is an important part of software developing and software engineering. Software testing plays a key role in error checking, correction, prevention and software quality assurance. With the improvement of software size and complexity, using the traditional software testing techniques to error checking and fault localization are also getting more and more difficult. Program Slicing, as presented by Mark Weiser (1) is an effective technique for narrowing the focus of attention to the relevant parts of a program, which decomposed programs by analyzing their data flow and control flow (2). Given a program point s and the variable set V, denoted by , is called a slicing criterion. Testing on the base of program slicing is to remove irrelevant statements from source codes according to slicing criterion while preserving the behavior of its original program, making the debugging can be attention to the relevant code to achieve the purpose of rapid fault localization.