Creation Assistant for Easy Assignment
Document Type
Oral Presentation
Campus where you would like to present
SURC 140
Start Date
16-5-2013
End Date
16-5-2013
Abstract
Software development projects receive many bug reports each day. Each of these reports needs to be examined and decisions made about how to handle the report. This process is called bug report triage. One decision that is frequently made is to which software developer to assign the bug report. There have been many efforts toward automating this decision, with the most promising approaches using machine learning algorithms. However, creating a bug report assignment recommender using machine learning is a complex process that must be tailored to each software development project. This project presents a tool, called the Creation Assistant for Easy Assignment (CASEA), which assists in creating a bug report assignment recommender for a software development project. CASEA uses data mining to pull reports from a Bugzilla bug repository via XML-RPC, assists in creating heuristics to know who fixed a bug, helps filter the data to recommend only current project developers, and creates a bug report assignment recommender using the SVM machine learning algorithm. Feedback on the effectiveness of the created recommender is provided using precision and recall metrics. The user can then adjust the filtering and heuristics until they are satisfied with the recommender performance. We evaluated CASEA by creating a recommender for the Eclipse IDE project and found that we could create an assignment recommender within 10 percent of the precision and recall of a hand tailored recommender. This software makes using a bug report assignment recommenders practical, potentially saving software development companies both time and money.
Recommended Citation
Burton, Hank; Brooks, Marshall; and Canada, Justin, "Creation Assistant for Easy Assignment " (2013). Symposium Of University Research and Creative Expression (SOURCE). 15.
https://digitalcommons.cwu.edu/source/2013/oralpresentations/15
Additional Mentoring Department
Computer Science
Creation Assistant for Easy Assignment
SURC 140
Software development projects receive many bug reports each day. Each of these reports needs to be examined and decisions made about how to handle the report. This process is called bug report triage. One decision that is frequently made is to which software developer to assign the bug report. There have been many efforts toward automating this decision, with the most promising approaches using machine learning algorithms. However, creating a bug report assignment recommender using machine learning is a complex process that must be tailored to each software development project. This project presents a tool, called the Creation Assistant for Easy Assignment (CASEA), which assists in creating a bug report assignment recommender for a software development project. CASEA uses data mining to pull reports from a Bugzilla bug repository via XML-RPC, assists in creating heuristics to know who fixed a bug, helps filter the data to recommend only current project developers, and creates a bug report assignment recommender using the SVM machine learning algorithm. Feedback on the effectiveness of the created recommender is provided using precision and recall metrics. The user can then adjust the filtering and heuristics until they are satisfied with the recommender performance. We evaluated CASEA by creating a recommender for the Eclipse IDE project and found that we could create an assignment recommender within 10 percent of the precision and recall of a hand tailored recommender. This software makes using a bug report assignment recommenders practical, potentially saving software development companies both time and money.
Faculty Mentor(s)
John Anvik