Big data in software engineering: A systematic literature review

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Selami Bagriyanik
Adem Karahoca


Purpose of Study: We investigate the big data studies using batch and/or streaming data generated in the process of software development lifecycle. All phases of application development phases are in our scope including but not limited to elicitation, requirements analysis, design, software implementation, version control management, unit / functional / regression / automated / performance / stress test, release management, application log monitoring,  application usage monitoring, user complaint management, security and compliance management and software problem management.Methods: We use a systematic literature review methodology used in Software Engineering studies to find and analyse the related studies published from January 2010 to October 2015. We synthesize the quantitative and qualitative outputs of selected papers and report the results.Findings and Results: In general, there are scarce studies in the literature. However there are relatively more papers regarding some areas such as Software Quality, Development, Project Management and Human Computer Interaction. However research in some fields such as Deployment, Requirements Engineering, Release Management and Mobile Applications were relatively less. Conclusions & Recommendations: More studies are required to identify the use cases, data attributes, measurements, platform requirements especially in the fields which are identified as having lack of study.  A holistic big data perspective is needed to support software engineering ecosystems in large and complex enterprises.

Keywords: Big Data, Software Engineering, Software Analytics, Data Mining, Software Development, Operational Intelligence, Software Archaeology


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How to Cite
Bagriyanik, S., & Karahoca, A. (2016). Big data in software engineering: A systematic literature review. Global Journal of Information Technology: Emerging Technologies, 6(1), 107–116.

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