Testing Software Using Swarm Intelligence: A Bee Colony Optimization Approach

• Main Authors: Omar El Ariss, Steve Bou ghosn, Weifeng Xu
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2016-12-01
• Series: EAI Endorsed Transactions on Collaborative Computing
• Subjects: and its automation is valuable. We propose a multi-objective search based test generation technique that is based on both functional and structural testing. Our Search Based Software Testing (SBST) technique is based on a bee colony optimization algorithm that integrates adaptive random testing from the functional side and condition/decision and multiple condition coverage from the structural side. The constructive approach that the bee colony algorithm uses for solution generation allows our SBST to address the limitations of previous approaches relying on fully random initial solutions and single objective evaluation. We perform extensive experimental testing to justify the effectiveness of our approach.; swarm intelligence; unit testing; automated test generation; branch coverage; search based testing
• Online Access: http://eudl.eu/doi/10.4108/eai.3-12-2015.2262529

Software testing is a critical activity in increasing our confidence of a system under test and improving its quality. The key idea for testing a software application is to minimize the number of faults found in the system. Software verification through testing is a crucial step in the application's development life cycle. This process can be regarded as expensive and laborious, and its automation is valuable. We propose a multi-objective search based test generation technique that is based on both functional and structural testing. Our Search Based Software Testing (SBST) technique is based on a bee colony optimization algorithm that integrates adaptive random testing from the functional side and condition/decision and multiple condition coverage from the structural side. The constructive approach that the bee colony algorithm uses for solution generation allows our SBST to address the limitations of previous approaches relying on fully random initial solutions and single objective evaluation. We perform extensive experimental testing to justify the effectiveness of our approach.