Predicting expert developers for newly reported bugs using frequent terms similarities of bug attributes

A software bug repository not only contains the data about software bugs, but also contains the information about the contribution of developers, quality engineers (testers), managers and other team members. It contains the information about the efforts of team members involved in resolving the software bugs. This information can be analyzed to identify some useful knowledge patterns. One such pattern is identifying the developers, who can help in resolving the newly reported software bugs. In this paper a new algorithm is proposed to discover experts for resolving the newly assigned software bugs. The purpose of proposed algorithm is two fold. First is to identify the appropriate developers for newly reported bugs. And second is to find the expertise for newly reported bugs that can help other developers to fix these bugs if required. All the important information in software bug reports is of textual data types like bug summary, description etc. The algorithm is designed using the analysis of this textual information. Frequent terms are generated from this textual information and then term similarity is used to identify appropriate experts (developers) for the newly reported software bug.