Software Risk Estimation Through Bug Reports Analysis and Bug-fix Time Predictions

Categorizing the level of software risk components is very important for software developers. This categorization allows the developers to increase software availability, security, and provide better project management process. This research proposes a novel approach risk estimation system that aims to help software internal stakeholders to evaluate the currently existing software risk by predicting a quantitative software risk value. This risk value is estimated using the earlier software bugs reports based on a comparison between current and upcoming bug-fix time, duplicated bugs records, and the software component priority level. The risk value is retrieved by using a machine learning on a Mozilla Core dataset (Networking: HTTP software component) using Tensorflow tool to predict a risk level value for specific software bugs. The total risk results ranged from 27.4% to 84% with maximum bug-fix time prediction accuracy of 35%. Also, the result showed a strong relationship for the risk values obtained from the bug-fix time prediction and showed a low relationship with the risk values from the duplicated bug records.