Static change impact analysis techniques: A comparative study

Sun, Xiaobing; Li, Bixin; Leung, Hareton; Li, Bin; Zhu, Jun

doi:10.1016/j.jss.2015.07.047

“…Previous studies have shown that a number of coupling measures, related to aggregation and invocation coupling, are related to a higher probability of common changes. This indicates that these coupling measures should be good indicators of ripple effects and are used as such in a decision model for ranking classes according to their probability to contain ripple effects associated with given change requests (Briand et al 1999;Wilkie and Kitchenham 2000;Sun et al 2015). According to Briand et al (1999), it is also clear that a substantial number of ripple effects are not covered by the selected highly coupled classes.…”

Section: Discussionmentioning

confidence: 99%

“…CIA techniques can be typically static or dynamic (Sun et al 2015), depending on how the information is collected to analyse its change impact. Dynamic techniques rely on information gathered during program execution to compute the change impact set while static techniques are centred around the source code, semantic information and change dependencies.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic techniques rely on information gathered during program execution to compute the change impact set while static techniques are centred around the source code, semantic information and change dependencies. Because of the many false positives and the effort required in dynamic analysis (collecting data during execution and analyzing data during execution), static techniques have gained popularity (Sun et al 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An empirical study on the interplay between semantic coupling and co-change of software classes

Ajienka

¹

,

Capiluppi

²

,

Counsell

³

2017

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Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

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“…Subtypes of software repositories mining include historical repositories analysis, and runtime repositories and code repositories analyses. Subtypes of coupling measurement include structural coupling, conceptual coupling, dynamic function coupling, and relational topic based coupling [13,16,[35][36][37]. Execution information analysis include offline and online paradigms [38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

An Integrated Model for Information Retrieval Based Change Impact Analysis

Wang

¹

,

He

²

,

Li

³

et al. 2018

Scientific Programming

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The paper presents an approach to combine multiple existing information retrieval (IR) techniques to support change impact analysis, which seeks to identify the possible outcomes of a change or determine the necessary modifications for affecting a desired change. The approach integrates a bag-of-words based IR technique, where each class or method is abstracted as a set of words, and a neural network based IR technique to derive conceptual couplings from the source code of a software system. We report rigorous empirical assessments of the changes of three open source systems: jEdit, muCommander, and JabRef. The impact sets obtained are evaluated at the method level of granularity, and the results show that our integrated approach provides statistically significant improvements in accuracy across several cut points relative to the accuracies provided by the individual methods employed independently. Improvements in F-score values of up to 7.3%, 10.9%, and 17.3% are obtained over a baseline technique for jEdit, muCommander, and JabRef, respectively.

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“…Software Change Impact Analysis (CIA) is an essential technique for identifying the potential ripple effects caused by software changes during software maintenance and evolution (Briand et al 1999;Wilkie and Kitchenham 2000). CIA techniques can be typically static or dynamic (Sun et al 2015), depending on how the information is collected to analyse its change impact. Dynamic techniques rely on information gathered during program execution to compute the change impact set while static techniques are centred around the source code, semantic information and change dependencies.…”

Section: Introductionmentioning

confidence: 99%

An empirical study on the interplay between semantic coupling and co-change of software classes

Ajienka

¹

,

Capiluppi

²

,

Counsell

³

2018

Proceedings of the 40th International Conference on Software Engineering

View full text Add to dashboard Cite

Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

show abstract

Static change impact analysis techniques: A comparative study

Cited by 18 publications

References 36 publications

An empirical study on the interplay between semantic coupling and co-change of software classes

An empirical study on the interplay between semantic coupling and co-change of software classes

An Integrated Model for Information Retrieval Based Change Impact Analysis

An empirical study on the interplay between semantic coupling and co-change of software classes

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