Search-based Resource Scheduling for Bug Fixing Tasks

Abstract

The software testing phase usually results in a large number of bugs to be fixed. The fixing of these bugs require executing certain activities (potentially concurrent) that demand resources having different competencies and workloads. Appropriate resource allocation to these bug-fixing activities can help a project manager to schedule capable resources to these activities, taking into account their availability and skill requirements for fixing different bugs. This paper presents a multi-objective search-based resource scheduling method for bug-fixing tasks. The inputs to our proposed method include i) a bug model, ii) a human resource model, iii) a capability matching method between bug-fixing activities and human resources and iv) objectives of bug-fixing. A genetic algorithm (GA) is used as a search algorithm and the output is a bug-fixing schedule, satisfying different constraints and value objectives. We have evaluated our proposed scheduling method on an industrial data set and have discussed three different scenarios. The results indicate that GA is able to effectively schedule resources by balancing different objectives. We have also compared the effectiveness of using GA with a simple hill climbing algorithm. The comparison shows that GA is able to achieve statistically better fitness values than hill-climbing.