Dependence clusters in source code

Harman, Mark; Binkley, David; Gallagher, Keith; Gold, Nicolas; Krinke, Jens

doi:10.1145/1596527.1596528

Cited by 40 publications

(53 citation statements)

References 94 publications

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“…The first one, Slice VV , is the traditional vertex-level slicing [16] where the slicing criterion is an SDG vertex and the result is a set of vertices, V. With such a slice the criteria is dependent on each of the vertices found in V. The second, Slice VF , has the same slicing criteria, a vertex, but produces a set of functions F rather than a set of vertices. In this case, the criteria is dependent on the (entry-point vertices of the) functions in F.…”

Section: A Program Slicingmentioning

confidence: 99%

“…Related Work Binkley and Harman [8] introduced the notion of dependence clusters and employed program slicing at the vertexlevel to identify them. Harman et al [16] later extended this initial study with a large-scale study of 45 programs and gave clustering definitions based on the direction of dependence (backward/forward). They found that vertex-level slice-based clusters were a common phenomenon in systems and empirically identified patterns of clustering.…”

Section: Threats To Validitymentioning

confidence: 99%

“…The current view is that large dependence clusters hinder many different software engineering activities, including impact analysis, maintenance, program comprehension and software testing [13], [16], [17]. In light of this view, this paper compares two approaches to clustering.…”

Section: Threats To Validitymentioning

confidence: 99%

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Uncovering dependence clusters and linchpin functions

Binkley

Beszedes

Islam

et al. 2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program's System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second.Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches.In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions -functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as effective proxies for the more expensive, finer-grained approaches.Finally, the linchpin analysis shows that linchpin functions can be effectively and automatically identified.

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Section: A Program Slicingmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

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Uncovering dependence clusters and linchpin functions

Binkley

Beszedes

Islam

et al. 2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

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“…A dependence cluster is a set of program elements that mutually depend on each other [46]. Their existence in source code has gained increasing attention recently because it has been demonstrated in various maintenance-related contexts that they may be detrimental to code comprehension, maintenance and evolution, impact analysis, and testing.…”

Section: Other Applicationsmentioning

confidence: 99%

Advances in software product quality measurement and its applications in software evolution

Hegedűs

2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…A dependence cluster is a set of program elements that all directly or transitively depend upon one another [8,18]. Prior empirical studies found that large dependence clusters are highly prevalent in software systems and further complicate many software activities such as software maintenance, testing, and comprehension [8,18].…”

Section: Introductionmentioning

confidence: 99%

An empirical study on dependence clusters for effort-aware fault-proneness prediction

Yang

Harman

Krinke

et al. 2016

Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering

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A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the functionlevel with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five wellknown open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters

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Dependence clusters in source code

Cited by 40 publications

References 94 publications

Uncovering dependence clusters and linchpin functions

Uncovering dependence clusters and linchpin functions

Advances in software product quality measurement and its applications in software evolution

An empirical study on dependence clusters for effort-aware fault-proneness prediction

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