Mining the maintenance history of a legacy software system

Shirabad, Jelber Sayyad; Lethbridge, Timothy C.; Matwin, Stan

doi:10.1109/icsm.2003.1235410

Cited by 29 publications

(9 citation statements)

References 9 publications

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“…The analysis was performed at the class level. Sayyad-Shirabad et al also mined the change history of a software system in the same fashion [8], but stayed at the file level.…”

Section: A Maintenance-oriented Approachesmentioning

confidence: 99%

Replaying IDE interactions to evaluate and improve change prediction approaches

Robbes

Pollet

Lanza

2010

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)

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Abstract-Change prediction helps developers by recommending program entities that will have to be changed alongside the entities currently being changed. To evaluate their accuracy, current change prediction approaches use data from versioning systems such as CVS or SVN. These data sources provide a coarse-grained view of the development history that flattens the sequence of changes in a single commit. They are thus not a valid basis for evaluation in the case of developmentstyle prediction, where the order of the predictions has to match the order of the changes a developer makes.We propose a benchmark for the evaluation of change prediction approaches based on fine-grained change data recorded from IDE usage. Moreover, the change prediction approaches themselves can use the more accurate data to fine-tune their prediction. We present an evaluation procedure and use it on several change prediction approaches, both novel and from the literature, and report on the results.

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“…The analysis was performed at the class level. Sayyad-Shirabad et al also mined the change history of a software system in the same fashion [8], but stayed at the file level.…”

Section: A Maintenance-oriented Approachesmentioning

confidence: 99%

Replaying IDE interactions to evaluate and improve change prediction approaches

Robbes

Pollet

Lanza

2010

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)

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“…None of these works on logical coupling did address its predictive power. Sayyad-Shirabad et al use inductive learning to learn different concepts of relevance between logically coupled files [21,22]. A concept is a set of attributes like file name, extension and simple metrics like number of routines defined.…”

Section: Related Workmentioning

confidence: 99%

Mining version histories to guide software changes

Zimmermann

Zeller

Weißgerber

et al. 2005

IIEEE Trans. Software Eng.

660

307

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“…[3], [9], [11]. If we analyze the used methodology, most of the studies investigating coupled changes use some kind of data mining for this purpose [12], [17], [20], [25], [26], [28], [29]. Here, the frequent item-sets technique is used to identify frequent changes in the source code [20], [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Which Change Sets in Git Repositories Are Related?

Ramadani¹,

Wagner²

2016

2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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Abstract-Software repositories contain valuable information about the history of software changes. Using data mining, researchers have identified file changes that happened together frequently to present hints for necessary changes to developers. However, not all file change sets are related. This can affect the recommendations about coupled file changes negatively by delivering irrelevant couplings to the developers. The commit time and branching characteristics of Git have not been investigated together in previous heuristics for grouping related change sets. We exploit the mappings between commit messages and issue ids for judging the relatedness of change sets. We propose a heuristic for Git and investigate the influence of two factors, the time between the commits and their branching on the relatedness of change sets using the repositories of five open-source systems using logistic regression. According to our findings, the combination of these two factors influences the relatedness of change sets. Individually measured, only the time significantly influences the relatedness, the branching itself does not. Our results support previous heuristic that also in Git repositories the commit time is important for grouping related change sets.

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Mining the maintenance history of a legacy software system

Cited by 29 publications

References 9 publications

Replaying IDE interactions to evaluate and improve change prediction approaches

Replaying IDE interactions to evaluate and improve change prediction approaches

Mining version histories to guide software changes

Which Change Sets in Git Repositories Are Related?

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