Summarizing Evolutionary Trajectory by Grouping and Aggregating relevant code changes

Jiang, Qingtao; Peng, Xin; Wang, Hai; Xing, Zhenchang; Zhao, Wenyun

doi:10.1109/saner.2015.7081846

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…When the software is put into use, it will continue to evolve and produce multiple versions. Each version often contains hundreds or thousands of revisions submitted by developers 11,12 . During the evolution process, new defects are inevitably introduced into the new version of software due to human factors.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Yu,

Zhu,

Han

et al. 2023

J Software Evolu Process

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Cross‐version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object‐oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes (RNC), the Ratio of Deleted Classes (RDC), the Average Ratio of Metric Change (ARMC), the Ratio of Label Changed Classes (RLCC), the Ratio of Unchanged Classes (RUC), and the Ratio of Interference Classes (RIC). An empirical study was conducted on 40 versions of 11 object‐oriented projects from the PROMISE repository. Precision, Recall, F‐measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC, RDC, and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F‐measure. RLCC and RIC show very strong or strong negative correlations with Recall and F‐measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.

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Section: Introductionmentioning

confidence: 99%

“…Each version often contains hundreds or thousands of revisions submitted by developers. 11,12 During the evolution process, new defects are inevitably introduced into the new version of software due to human factors. Therefore, software evolution is also one of the important sources of software defects.…”

mentioning

confidence: 99%

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Yu,

Zhu,

Han

et al. 2023

J Software Evolu Process

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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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Recording and replaying system specific, source code transformations

Santos

Etien

Anquetil

et al. 2015

2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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During its lifetime, a software system is under continuous maintenance to remain useful. Maintenance can be achieved in activities such as adding new features, fixing bugs, improving the system's structure, or adapting to new APIs. In such cases, developers sometimes perform sequences of code changes in a systematic way. These sequences consist of small code changes (e.g., create a class, then extract a method to this class), which are applied to groups of related code entities (e.g., some of the methods of a class). This paper presents the design and proof-of-concept implementation of a tool called MACRORECORDER. This tool records a sequence of code changes, then it allows the developer to generalize this sequence in order to apply it in other code locations. In this paper, we discuss MACRORECORDER's approach that is independent of both development and transformation tools. The evaluation is based on previous work on repetitive code changes related to rearchitecting. MACRORECORDER was able to replay 92% of the examples, which consisted in up to seven code entities modified up to 66 times. The generation of a customizable, large-scale transformation operator has the potential to efficiently assist code maintenance.

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Summarizing Evolutionary Trajectory by Grouping and Aggregating relevant code changes

Cited by 3 publications

References 18 publications

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

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

Recording and replaying system specific, source code transformations

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