Network analysis of software change history for understanding software evolution

Kanwal, Jaweria; Maqbool, Onaiza; Abbasi, Rabeeh Ayaz; Abbasi, Abdul Qudus

doi:10.1109/inmic.2014.7097342

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Network science has many applications like in: community detection [22][23], influence propagation [24][25], event detection [26]- [29], user characterization [30], [31], education [32][3], linguistics [33], sentiment analysis [34], [35], analyzing money laundering [36], software engineering [37], altmetrics [38]- [40], archeology [41], disaster management [42], tourism [43], socio-economics [4], and advertising [44].…”

Section: Related Workmentioning

confidence: 99%

Analyzing Public Discussions About #SaudiWomenCanDrive Using Network Science

et al. 2022

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Section: Related Workmentioning

confidence: 99%

Analyzing Public Discussions About #SaudiWomenCanDrive Using Network Science

et al. 2022

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“…The metrics are analyzed for a few systems: Mozilla Rhino, JUnit, Elastic-Search, RSpec (core package), Tomcat, ION, and the Linux kernel. Kanwal et al (2014) use network analysis metrics to analyze evolution of the files that are change together in a commit. A network of files is created on the basis of their co-occurrence in various commits.…”

Section: Changes In Code and Other Artifactsmentioning

confidence: 99%

Open Source Software Evolution

Chahal

Saini

2016

International Journal of Open Source Software and Processes

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Due to the dominance of Open Source Software (OSS) in IT and the IT enabled services industry, various stakeholders are keen to understand the OSS evolution process. Several studies have been conducted in the past in this regard. There are various techniques used in the past for understanding OSS evolution process from different perspectives. This paper reports a systematic literature review on the topic in order to understand its current state and to identify opportunities for future. This research identified 190 studies, selected against a set of questions, for discussion. It categorizes the research studies into nine categories. We report the review results in a set of two papers. This paper discusses the research results of the techniques used for OSS evolution analysis only i.e. one out of the nine categories. A subsequent paper carries discussion on the remaining categories. Based on the results obtained from the systematic review, there is evidence of shift in the metrics and methods for OSS evolution analysis over the period of time. OSS systems were found to grow at a super linear rate in the initial studies. But later studies revealed that branches of an OSS system grow at different rates. However, more number of studies should be carried out using a repeatable methodology in order to obtain well-formed and generalizable results.

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