Exploring trends in the evolution of open-source systems

Shatnawi, Raed

doi:10.1007/s13198-019-00901-x

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Shatnavi [23] investigated the growth rate in the evolution of five open‐source software systems using linear and non‐linear models. They found non‐linear models more common at both class and system levels and a positive growth rate with time.…”

Section: Related Workmentioning

confidence: 99%

Predicting co‐change probability in software applications using historical metadata

Agrawal

Singh

2020

IET softw.

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It is quite challenging to track the after‐effects of changes with increased dependencies among classes while making a change in software applications. Software change impact analysis aims to identify classes affected by a change in software applications. In recent years, researchers have found that revision history has great potential for identifying evolutionary coupling. The two main factors affecting the prediction results are the length and the age of change history considered for deriving dependencies. The effect of age of change history on co‐change prediction results in software applications is empirically studied by varying the weightage of change commits. The results indicate that the older commits have less influence in deriving dependent classes than the newer ones. The proposed approach is useful for software effort estimation and identifying dependencies during the software development, testing, and maintenance phase.

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Section: Related Workmentioning

confidence: 99%