Impact Analysis by Mining Software and Change Request Repositories

Cerulo, Luigi

doi:10.1109/metrics.2005.28

Cited by 134 publications

(87 citation statements)

References 15 publications

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“…IR-based methods have been applied to support practical tasks. For instance, IR methods have been successfully used to support feature location (Liu et al 2007;Eaddy et al 2008;Revelle and Poshyvanyk 2009;Revelle et al 2010;Dit et al 2012a;Dit et al 2012b;, traceability link recovery (Antoniol et al 2002;Hayes et al 2006;De Lucia et al 2007;Cleland-Huang et al 2010;Oliveto et al 2010;Gethers et al 2011), and impact analysis (Antoniol et al 2000;Canfora and Cerulo 2005;Poshyvanyk et al 2009;Gethers and Poshyvanyk 2010;Gethers et al 2012). We do not discuss other applications of IR-based techniques in the context of software maintenance due to space limitations; however, interested readers are referred to (Binkley and Lawrie 2010a;Binkley and Lawrie 2010b) for such an overview.…”

Section: Conceptual Information In Softwarementioning

confidence: 99%

“…Association rules based on itemset mining were formed from the change-sets and used for changeprediction. Canfora et al (Canfora and Cerulo 2005) used the bug descriptions and the CVS commit messages for the purpose of change prediction. An information retrieval method is used to index the changed files, and commit logs, in the CVS and the past bug reports from the Bugzilla repositories.…”

Section: Evolutionary Information In Software Repositoriesmentioning

confidence: 99%

“…Decades of research efforts have produced a wide spectrum of approaches, ranging from the traditional static and dynamic analysis techniques Law and Rothermel 2003;Orso et al 2004;Ren et al 2004;Robillard 2005;Petrenko and Rajlich 2009) to the contemporary methods such as those based on Information Retrieval (IR) (Canfora and Cerulo 2005;Hill et al…”

Section: Introductionmentioning

confidence: 99%

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Integrating conceptual and logical couplings for change impact analysis in software

2012

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The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches.A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation.The results show that a combination of conceptual and evolutionary techniques, across several cutoff points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14%(from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.

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Section: Conceptual Information In Softwarementioning

confidence: 99%

Section: Evolutionary Information In Software Repositoriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating conceptual and logical couplings for change impact analysis in software

2012

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“…In impact analysis [2,4] the textual information indexed with the IR method is usually used in conjunction with structural information or historical information about changes. One of the main differences between these applications of IR vs. concept location is that they use queries extracted from different artifacts, rather than user written.…”

Section: Related Workmentioning

confidence: 99%

On the use of relevance feedback in IR-based concept location

Haiduc

Marcus

Menzies

2009

2009 IEEE International Conference on Software Maintenance

143

107

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“…There are few studies which investigate a Git version control system (Bird et al, 2009;Carlsson, 2013). Other studies combine more than one data source to be investigated, like the version control system and an issue tracking system (Canfora & Cerulo, 2005;D'Ambros, Lanza & Robbes, 2009;Fischer, Pinzger & Gall, 2003;Wu et al, 2011) where the data extracted from these two sources is analyzed and the link between the changed files and issues is determined. We use three different sources for the additional attributes: the Git versioning history, the JIRA issue tracking system and the software documentation.…”

Section: Related Workmentioning

confidence: 99%

Are suggestions from coupled file changes useful for perfective maintenance tasks?

Ramadani

Wagner

2017

PeerJ Computer Science

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Background. Software maintenance is an important activity in the development process where maintenance team members leave and new members join over time. The identification of files which are changed together frequently has been proposed several times. Yet, existing studies about coupled file changes ignore the feedback from developers as well as the impact of these changes on the performance of maintenance and rather these studies rely on the analysis findings and expert evaluation. Methods. We investigate the usefulness of coupled file changes during perfective maintenance tasks when developers are inexperienced in programming or when they were new on the project. Using data mining on software repositories we identify files that are changed most frequently together in the past. We extract coupled file changes from the Git repository of a Java software system and join them with corresponding attributes from the versioning and issue tracking system and the project documentation. We present a controlled experiment involving 36 student participants in which we investigate if coupled file change suggestions influence the correctness of the task solutions and the required time to complete them. Results. The results show that the use of coupled file change suggestions significantly increases the correctness of the solutions. However, there is only a minor effect on the time required to complete the perfective maintenance tasks. We also derived a set of the most useful attributes based on the developers' feedback. Discussion. Coupled file changes and a limited number of the proposed attributes are useful for inexperienced developers working on perfective maintenance tasks where although the developers using these suggestions solved more tasks, they still need time to understand and organize this information.

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Impact Analysis by Mining Software and Change Request Repositories

Cited by 134 publications

References 15 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

On the use of relevance feedback in IR-based concept location

Are suggestions from coupled file changes useful for perfective maintenance tasks?

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