Software quality prediction using case-based reasoning

The ability to efficiently prevent faults in large software systems is a very important concern of software project managers. Successful testing allows us to build quality software systems. Unfortunately, it is not always possible to effectively test a system due to time, resources, or other constraints. A critical bug may cause catastrophic consequences, such as loss of life or very expensive equipment. We can facilitate testing by finding where faults are more likely to be hidden. Case-Based Reasoning (CBR) is one of many methodologies that make this process faster and cheaper by discovering faults early in the software life cycle. This is one of the methodologies used to predict software quality of the system by discovering fault-prone modules. We employ the SMART tool to facilitate CBR , using product and process metrics as independent variables. The study found that CBR is a robust tool capable of carrying out software quality prediction on its own with acceptable results. We also show that CBR's weaknesses do not hinder its effectiveness in finding misclassified modules.