Understanding systematic and collaborative code changes by mining evolutionary trajectory patterns

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

doi:10.1002/smr.1840

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…However, ChangeDistiller cannot generate fine-grained scripts on programming languages that have a lot of composite elements in statements (such as C++). Jiang et al [39] proposed an approach to aggregate relevant code changes that were committed through the history from version control systems using change operations and locations.…”

Section: Related Workmentioning

confidence: 99%

A study of code change patterns for adaptive maintenance with AST analysis

Meqdadi¹,

Aljawarneh²

2020

IJECE

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Example-based transformational approaches to automate adaptive maintenance changes plays an important role in software research. One primary concern of those approaches is that a set of good qualified real examples of adaptive changes previously made in the history must be identified, or otherwise the adoption of such approaches will be put in question. Unfortunately, there is rarely enough detail to clearly direct transformation rule developers to overcome the barrier of finding qualified examples for adaptive changes. This work explores the histories of several open source systems to study the repetitiveness of adaptive changes in software evolution, and hence recognizing the source code change patterns that are strongly related with the adaptive maintenance. We collected the adaptive commits from the history of numerous open source systems, then we obtained the repetitiveness frequencies of source code changes based on the analysis of Abstract Syntax Tree (AST) edit actions within an adaptive commit. Using the prevalence of the most common adaptive changes, we suggested a set of change patterns that seem correlated with adaptive maintenance. It is observed that 76.93% of the undertaken adaptive changes were represented by 12 AST code differences. Moreover, only 9 change patterns covered 64.69% to 76.58% of the total adaptive change hunks in the examined projects. The most common individual patterns are related to initializing objects and method calls changes. A correlation analysis on examined projects shows that they have very similar frequencies of the patterns correlated with adaptive changes. The observed repeated adaptive changes could be useful examples for the construction of transformation approaches

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Section: Related Workmentioning

confidence: 99%

A study of code change patterns for adaptive maintenance with AST analysis

Meqdadi¹,

Aljawarneh²

2020

IJECE

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“…Much prior research [15,17,18,21] has studied co-change patterns in maintenance. Silva et al [21] studied co-changing les software maintenance and classi ed them into 3 patterns: Encapsulated, Crosscutting, and Octopus.…”

Section: Co-change Patterns In Maintenancementioning

confidence: 99%

“…Jiang et el. [15] presented an approach to group and aggregate relevant code changes as six types of trajectory patterns and detected underlying rules with these patterns. Nguyen et al [18] studied repetitive changes within and across projects by comparing old and new AST subtrees.…”

Section: Co-change Patterns In Maintenancementioning

confidence: 99%

“…Negara [17] proposed a new algorithm to mine previously unknown patterns from ne-grained sequences of code changes; they summarized and evaluated 10 popular types of transformations. Jiang et el [15] presented an approach to group and aggregate relevant code changes into six types of trajectory patterns and detected underlying rules with these patterns. While patterns in a group of either structurally-related or co-changing les have been extracted, there is little study characterizing co-changing les as a community and exploring the evolution of this community.…”

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confidence: 99%

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The birth, growth, death and rejuvenation of software maintenance communities

Feng

Cai

Kazman

et al. 2018

Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

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Background: Though much research has been conducted to investigate software maintenance activities, there has been little work charactering maintenance les as a community and exploring the evolution of this community. Aims: The goal of our research is to identify maintenance communities and monitor their evolutionbirth, growth, death and rejuvenation. Method: In this paper, we leveraged a social community detection algorithm-clique prelocation method (CPM)-to identify le communities. Then we implemented an algorithm to detect new communities, active communities, inactive communities and reactivated communities by cumulatively detecting and constantly comparing communities in time sequences. Results: Based on our analysis of 14 open-source projects, we found that new communities are mostly caused by bug and improvement issues. An active community can be vigorous, on and o , through the entire life of a system, and so does an inactive community. In addition, an inactive community can be reactivated again, mostly through bug issues. Conclusions: These ndings add to our understanding of software maintenance communities and help us identify the most expensive maintenance spots by identifying constantly active communities. CCS CONCEPTS• Software and its engineering → Software evolution;

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“…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.…”

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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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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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Understanding systematic and collaborative code changes by mining evolutionary trajectory patterns

Cited by 6 publications

References 22 publications

A study of code change patterns for adaptive maintenance with AST analysis

A study of code change patterns for adaptive maintenance with AST analysis

The birth, growth, death and rejuvenation of software maintenance communities

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

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