Dependence communities in source code

Hamilton, James; Danicic, Sebastian

doi:10.1109/icsm.2012.6405325

“…We think that further research is needed to find out the connection of dependence clusters and the structures identified by methods of community structure analysis on software dependence graphs [19,20]. Since this method is based on probabilities and is "less strict" we could possibly reuse related results in linchpin identification.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, it seems that dependence clusters are independent of the programming language and the type of the system [3,17,18]. An interesting approach to locate interrelated program elements in programs is based on applying community structure analysis on software dependence graphs [19,20]. A set of program elements are treated to form a community if the number of internal connections is more than expected given the overall distribution of the connections in the whole program.…”

Section: Previous and Related Workmentioning

confidence: 99%

Empirical investigation of SEA-based dependence cluster properties

Beszedes

¹

,

Schrettner

²

,

Csaba

³

et al. 2015

Science of Computer Programming

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Dependence clusters are (maximal) groups of source code entities that each depend on the other according to some dependence relation. Such clusters are generally seen as detrimental to many software engineering activities, but their formation and overall structure are not well understood yet. In a set of subject programs from moderate to large sizes, we observed frequent occurrence of dependence clusters using Static Execute After (SEA) dependences (SEA is a conservative yet efficiently computable dependence relation on program procedures). We identified potential linchpins; these are procedures that can primarily be made responsible for keeping the cluster together. Furthermore, we found that as the size of the system increases, it is more likely that multiple procedures are jointly responsible as sets of linchpins. We also give a heuristic method based on structural metrics for locating possible linchpins as their exact identification is unfeasible in practice, and presently there are no better ways than the brute-force method. We defined novel metrics to be able to uncover clusters of different sizes in programs, and also to relate programs in terms of their degree of clusterization. Finally, we present a possible application of SEA-based dependences in change impact analysis, and investigate the effect of dependence clusters on the successfulness of this activity.

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“…This set of algorithms is originally defined on (possibly directed and weighted) graphs that represent complex networks (social, biological, technological, etc. ), and recently have also been suggested for software engineering problems [13], [14]. Community structures are detected based on statistical information about the number of edges between sets of graph nodes.…”

Section: A Community Based Clusteringmentioning

confidence: 99%

Are My Unit Tests in the Right Package?

Balogh

¹

,

Gergely

²

,

Beszedes

³

et al. 2016

2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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The software development industry has adopted written and de facto standards for creating effective and maintainable unit tests. Unfortunately, like any other source code artifact, they are often written without conforming to these guidelines, or they may evolve into such a state. In this work, we address a specific type of issues related to unit tests. We seek to automatically uncover violations of two fundamental rules: 1) unit tests should exercise only the unit they were designed for, and 2) they should follow a clear packaging convention. Our approach is to use code coverage to investigate the dynamic behaviour of the tests with respect to the code elements of the program, and use this information to identify highly correlated groups of tests and code elements (using community detection algorithm). This grouping is then compared to the trivial grouping determined by package structure, and any discrepancies found are treated as "bad smells." We report on our related measurements on a set of large open source systems with notable unit test suites, and provide guidelines through examples for refactoring the problematic tests.

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“…Furthermore, it seems that dependence clusters are independent of the programming language and the type of the system [3,17,18]. An interesting approach to locate interrelated program elements in programs is based on applying community structure analysis on software dependence graphs [19,20]. A set of program elements are treated to form a community if the number of internal connections is more than expected given the overall distribution of the connections in the whole program.…”

Section: Previous and Related Workmentioning

confidence: 99%

Empirical investigation of SEA-based dependence cluster properties

Beszedes

¹

,

Schrettner

²

,

Csaba

³

et al. 2013

2013 IEEE 13th International Working Conference on Source Code Analysis and Manipulation (SCAM)

1

0

View full text Add to dashboard Cite

Dependence clusters are (maximal) groups of source code entities that each depend on the other according to some dependence relation. Such clusters are generally seen as detrimental to many software engineering activities, but their formation and overall structure are not well understood yet. In a set of subject programs from moderate to large sizes, we observed frequent occurrence of dependence clusters using Static Execute After (SEA) dependences (SEA is a conservative yet efficiently computable dependence relation on program procedures). We identified potential linchpins; these are procedures that can primarily be made responsible for keeping the cluster together. Furthermore, we found that as the size of the system increases, it is more likely that multiple procedures are jointly responsible as sets of linchpins. We also give a heuristic method based on structural metrics for locating possible linchpins as their exact identification is unfeasible in practice, and presently there are no better ways than the brute-force method. We defined novel metrics to be able to uncover clusters of different sizes in programs, and also to relate programs in terms of their degree of clusterization. Finally, we present a possible application of SEA-based dependences in change impact analysis, and investigate the effect of dependence clusters on the successfulness of this activity.

show abstract

Dependence communities in source code

Cited by 10 publications

References 127 publications

Empirical investigation of SEA-based dependence cluster properties

Empirical investigation of SEA-based dependence cluster properties

Are My Unit Tests in the Right Package?

Empirical investigation of SEA-based dependence cluster properties

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