SE&lt;sup&gt;2&lt;/sup&gt; model to support software evolution

Kagdi, Huzefa; Gethers, Malcom; Poshyvanyk, Denys

doi:10.1109/icsm.2011.6080820

Cited by 4 publications

(3 citation statements)

References 17 publications

(17 reference statements)

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“…In addition, conceptual information has been utilized in conjunction with evolutionary data to support several other tasks, such as assigning incoming bug reports to developers (Anvik et al 2006;Jeong et al 2009;Kagdi and Poshyvanyk 2009;Kagdi et al 2011;Kagdi et al 2012), identifying duplicate bug reports (Runeson et al 2007;Wang et al 2008), estimating time to fix incoming bugs (Weiss et al 2007) and classifying software maintenance requests (Di Lucca et al 2002). Finally, we conducted a comprehensive literature survey on MSR approaches during the prologue of this work ).…”

Section: Evolutionary Information In Software Repositoriesmentioning

confidence: 99%

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: Evolutionary Information In Software Repositoriesmentioning

confidence: 99%

Integrating conceptual and logical couplings for change impact analysis in software

2012

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“…Growing and changing rates are the two most important factors in software evolution study [14], [15], [16], [17]. Gall et al [18] studied 20 different releases of a system with different major and minor releases.…”

Section: Related Workmentioning

confidence: 99%

How Often does a Source Code Unit Change within a Release Window?

Shobe

Karim

Kagdi

2015

Proceedings of the 8th India Software Engineering Conference

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To form a training set for a source-code change prediction model, e.g., using the association rule mining or machine learning techniques, commits from the source code history are needed. The traceability between releases and commits would facilitate a systematic choice of history in units of the project evolution scale (i.e., commits that constitute a software release). For example, the major release 25.0 in Chrome is mapped to the earliest revision 157687 and latest revision 165096 in the trunk. Using this traceability, an empirical study is reported on the frequency distribution of file changes for different release windows. In Chrome, the majority (50%) of the committed files change only once between a pair of consecutive releases. This trend is reversed after expanding the window size to at least 10. That is, the majority (50%) of the files change multiple times when commits constituting 10 or greater releases are considered. These results suggest that a training set of at least 10 releases is needed to provide a prediction coverage for majority of the files.

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“…In [16] Thomas captured the evolution information related to topic model and validated its usefulness. In [17] Kagdi proposed the SE2 model to support software evolution.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Model Driven Software Architecture Evolution Information Capture

Hui

2015

2015 2nd International Conference on Information Science and Control Engineering

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Software Architecture often evolves to meet the new requirement in component based software development. For understanding the nature of SA evolution and improve its quality, in this paper, we propose a model driven methodology to record and analyze the software architecture evolution information. That is, we record evolution information by using Extended Component Description Language and Ontology, and then implement a translation algorithm for mapping the evolution information into the software configuration system.

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SE<sup>2</sup> model to support software evolution

Cited by 4 publications

References 17 publications

Integrating conceptual and logical couplings for change impact analysis in software

Integrating conceptual and logical couplings for change impact analysis in software

How Often does a Source Code Unit Change within a Release Window?

Model Driven Software Architecture Evolution Information Capture

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