Learning dependency-based change impact predictors using independent change histories

Abdeen, Hani; Bali, Khaled M.; Sahraoui, Houari; Dufour, Bruno

doi:10.1016/j.infsof.2015.07.007

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…Not semantically coupled classes should also be not-structurally coupled classes. Previous research has demonstrated that this is not the case [3], [4], [8]. While previous studies have focused on combining structural and semantic coupling information with minimal benefits, this study argues that combining them because they are not consistent is an issue in itself.…”

Section: Introductionmentioning

confidence: 72%

“…Therefore, developers will miss a significant number of them by relying on source code information alone during change impact analysis for example [18]. The influence of semantic and structural coupling upon each other has not been studied, despite the fact that an analysis of only semantic dependencies during CIA will not reveal some structural dependencies and vice-versa [3], [4], [8].…”

Section: Hidden Dependenciesmentioning

confidence: 99%

“…Over the years, software engineering researchers have proposed tools and techniques to predict where those impact sets will arise. Most approaches rely on the software dependency information derived from static and dynamic analysis [1], [3], [4]. Others have focused on the root causes of these ripple effects and on predicting co-change of classes [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing Hidden Dependencies in OO Software: A Study Based on Open Source Projects

Ajienka

Capiluppi

Counsell

2017

2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Abstract-Dependency-based software change impact analysis is the domain concerned with estimating the sets of artifacts impacted by a change to a related artifact. Research has shown that analysing the various class dependency types independently will never completely reveal the impact sets. Therefore, dependency types are combined to improve the precision of estimated when compared to impact sets.Software classes can be linked in different ways; for instance semantically, if their meaning is somewhat related or, structurally, if one class depends on the services of other classes. 'Hidden' dependencies arise when two classes, linked structurally, do not share the same semantic namespace or when semantically dependent classes do not share a structural link.With the goal of revealing hidden dependencies during change impact analysis, we empirically investigated the relationship between structural and semantic class dependencies in objectoriented software systems.Results show that (i) semantic and structural links are significantly associated, (ii) the strengths of those links do not play a significant role and, (iii) a significant number of dependencies are hidden.We propose two refactoring techniques to deal with hidden dependencies, based on existing design patterns. We plan to investigate them further to assert whether either has the potential for reducing refactoring and testing effort.

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Section: Introductionmentioning

confidence: 72%

Section: Hidden Dependenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Managing Hidden Dependencies in OO Software: A Study Based on Open Source Projects

Ajienka

Capiluppi

Counsell

2017

2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…Using only semantic coupling information to predict co-evolution will produce a prediction model with low precision. Previous research by Abdeen et al (2015) has shown that combining semantic and structural coupling information when predicting change impact sets outperforms using either of them individually. However, semantic coupling metrics produced better recall values compared to structural coupling metrics.…”

Section: Summary On Rq2 and Its Resultsmentioning

confidence: 99%

“…However we cannot firmly assert that using the semantic coupling metrics between classes to infer the strength of their co-change is a realistic objective as our empirical study did not show a linear relationship between the strengths of semantic and logical coupling. But we believe that using a combination of structural and semantic information to predict co-change patterns (Abdeen et al 2015) might be a more feasible objective.…”

Section: Discussionmentioning

confidence: 99%

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

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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Exploiting Results of Model-Based Analysis Tools

Durán

Gogolla

Guerra

et al. 2021

Composing Model-Based Analysis Tools

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Learning dependency-based change impact predictors using independent change histories

Cited by 18 publications

References 14 publications

Managing Hidden Dependencies in OO Software: A Study Based on Open Source Projects

Managing Hidden Dependencies in OO Software: A Study Based on Open Source Projects

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

Exploiting Results of Model-Based Analysis Tools

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