TY - GEN
T1 - Visually exploring software maintenance activities
AU - Levin, Stanislav
AU - Yehudai, Amiram
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Lehman's Laws teach us that a software system will become progressively less satisfying to its users over time, unless it is continually adapted to meet new needs. A line of previous works sought to better understand software maintenance by studying how commits can be classified into three main software maintenance activities. Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. In this work we suggest visualizations for exploring software maintenance activities in both project and individual developer scopes. We demonstrate our approach using a prototype we have built using the Shiny R framework. In addition, we have also published our prototype as an online demo. This demo allows users to explore the maintenance activities of a number of popular open source projects. We believe that the visualizations we provide can assist practitioners in monitoring and maintaining the health of software projects. In particular, they can be useful for identifying general imbalances, peaks, deeps and other anomalies in projects' and developers' maintenance activities.
AB - Lehman's Laws teach us that a software system will become progressively less satisfying to its users over time, unless it is continually adapted to meet new needs. A line of previous works sought to better understand software maintenance by studying how commits can be classified into three main software maintenance activities. Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. In this work we suggest visualizations for exploring software maintenance activities in both project and individual developer scopes. We demonstrate our approach using a prototype we have built using the Shiny R framework. In addition, we have also published our prototype as an online demo. This demo allows users to explore the maintenance activities of a number of popular open source projects. We believe that the visualizations we provide can assist practitioners in monitoring and maintaining the health of software projects. In particular, they can be useful for identifying general imbalances, peaks, deeps and other anomalies in projects' and developers' maintenance activities.
KW - Predictive Modeling
KW - Software Evolution
KW - Software Maintenance
UR - http://www.scopus.com/inward/record.url?scp=85075897600&partnerID=8YFLogxK
U2 - 10.1109/VISSOFT.2019.00021
DO - 10.1109/VISSOFT.2019.00021
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AN - SCOPUS:85075897600
T3 - Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019
SP - 110
EP - 114
BT - Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019
Y2 - 30 September 2019 through 1 October 2019
ER -