Using contextual information to predict co-changes

Wiese, Igor; Reginaldo, R; Steinmacher, Igor; Kuroda, Rodrigo Takashi; Oliva, Gustavo Ansaldi; Gerosa, Marco Aurélio

doi:10.1016/j.jss.2016.07.016

Cited by 21 publications

(43 citation statements)

References 15 publications

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“…Our research is defined as an external and conceptual replication of the baseline study published in [3]. In [11], authors discuss all the types of replication studies and divide them into two categories: "internal" and "external".…”

Section: Methodsmentioning

confidence: 99%

“…We then built co-change prediction models for a subset of files based on their ranking over commits, and associated co-changed files. Our study differs from the original study [3] in terms of experimental protocol in selecting co-changed file pairs, model construction, and performance evaluation.…”

Section: Introductionmentioning

confidence: 93%

“…Wiese et al [3] conducted an empirical study to observe whether change patterns could be identified using contextual information from both issue tracking and version control systems. They built predictive models using Random Forest classifier that is trained with the previous changes of file pairs, and recommends whether the second file in the pair should be changed when the first file in the pair has changed.…”

Section: Baseline Studymentioning

confidence: 99%

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Predicting Co-Changed Files: An External, Conceptual Replication

Tosun

Romero

2019

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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A software project can be comprised of several, highly connected files. A software developer may not know the files that are connected to which are developed or that are changed by another developer. This may induce faults by missing necessary edits on all related files. We build a prediction model for identifying files that should be edited together during a code change, and evaluate the performance of our model on two Apache projects' development history over more than 10 years. We conduct an external, conceptual replication study based on Wiese et al.'s prior work on predicting co-changed files. Our study shares the same goal but differentiates the experimental design in terms of data set construction, selection of file pairs, feature selection and the model output. Our prediction model's results, although the same performance measures are used, are much lower than what is reported in Wiese et al.'s study, mainly due to the differences in calculating these measures. The models evaluated at commit granularity could achieve 20% and 45% lower recall and precision rates, respectively, than those aggregated over all filepairs. Although it is practically more useful, predicting all files that will be co-changed together during a commit is more challenging than predicting whether a particular file will be changed in that commit. More information about the context of a co-change, the degree of centrality of a file in the project, or project characteristics could reveal more insights in building such predictors in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Baseline Studymentioning

confidence: 99%

See 1 more Smart Citation

Predicting Co-Changed Files: An External, Conceptual Replication

Tosun

Romero

2019

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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“…Out of these 3 transactions, 2 also included changes to the class C. Therefore, the support for the logical dependency A → C will be 2. By its own nature, support is a symmetric metric, so the A → C dependency also implies A ← C. The support value of a given rule determines how evident the rule is Wiese et al (2017).…”

Section: Operationalisationmentioning

confidence: 99%

“…In other words, the confidence is directional, and determines the strength of the consequence of a given (directional) logical dependency. The confidence value is the strength of a given association rule (Wiese et al 2017).…”

Section: Operationalisationmentioning

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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