In open source software project, a reported bug consist of many attributes. Contributors of open source projects also request for new features and feature enhancement. A software project need to be released in a given time frame(time based release) or after a number of features implemented in the software(feature based release). All the attributes of the bug and release time can be predicted using machine learning, deep learning and using mathematical modeling approach. Our objective is to develop entropy and crowd source based models by considering the uncertainty and irregularity. The developed models can assist in open source software evolution . These models will be validated using software repositories data . The models will help in maintaining the quality of software and hence in software evolution.
Jawaharlal Nehru University, New Delhi
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