The Link between Dependency and Cochange: Empirical Evidence

Geipel, Markus M.; Schweitzer, Frank

doi:10.1109/tse.2011.91

Cited by 28 publications

(112 citation statements)

References 37 publications

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“…According to Geipel and Schweitzer (2012), this indirectly means that any model that tries to infer structural coupling from logical coupling or co-evolution will produce a lot of false positives. On the other hand, using the structural coupling information between pairs of classes to infer their future co-change is a more realistic objective .…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies Gerosa 2011, 2015;Geipel and Schweitzer 2012;Fluri et al 2005) have shown that it is possible that structural and logical coupling are caused by other types of relationships (e.g., conceptual dependencies); some logically coupled classes were without structural coupling links between them and vice versa. Kagdi et al (2010Kagdi et al ( , 2013 demonstrated that finer granularity decreased the accuracy of all approaches; however, it does not prevent the combination of the two from outperforming the standalone techniques.…”

Section: The Link Between Semantic and Logical Couplingmentioning

confidence: 99%

“…Other studies, specifically addressed at establishing a link between coupling and co-change, have found that the set of co-changed classes was much larger compared to the set of structurally coupled classes Gerosa 2011, 2015;Fluri et al 2005, Geipel andSchweitzer 2012). This implies that not all of the change dependencies are related to structural dependencies and there could be other reasons for software artefacts to be change dependent (Oliva and Gerosa 2011).…”

Section: Introductionmentioning

confidence: 98%

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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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Section: Discussionmentioning

confidence: 99%

Section: The Link Between Semantic and Logical Couplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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

2017

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“…Geipel and Schweitzer [5] state that there is a directed dependency between classes X and Y if X depends on Y in such a way that X is not operational without class Y [11]. In the case of Java classes, this means that X will not compile in the absence of Y.…”

Section: A Structural Dependenciesmentioning

confidence: 99%

“…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%

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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Do code data sharing dependencies support an early prediction of software actual change impact set?

Liu

Huang

Egyed

et al. 2018

J Software Evolu Process

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Existing studies have shown that structural dependencies within code are good predictors for code actual change impact set-a set of entities that repeatedly changing together to ensure a consistent and complete change. However, the result is far from ideal, particularly when insufficient historical data are available at an early stage of software development. This paper demonstrates that a better understanding of data dependencies in addition to call dependencies greatly improves actual change impact set prediction. We propose a new approach and tool (namely, CHIP) to predict software actual change impact sets leveraging both call and data sharing dependencies.For this purpose, CHIP employs novel extensions (dependency frequency filtering and shared data type idf filtering) to reduce false positives. CHIP assumes that developers know initial places where to start making changes in the source code even though they may not know all changes. This approach has been empirically evaluated on 4 large-scale open source systems. Our evaluation demonstrates that data sharing dependencies have a complementary impact on software actual change impact set prediction as compared with predictions based on call dependencies only. CHIP improves the F2-score compared with the predictors using both Program Dependence Graph and evolutionary couplings. KEYWORDSdata sharing dependency, software impact analysis, source code dependency

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The Link between Dependency and Cochange: Empirical Evidence

Cited by 28 publications

References 37 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

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

Do code data sharing dependencies support an early prediction of software actual change impact set?

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