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Usability defects test escapee can have a negative impact on the success of software. It is quite common for projects to have a tight timeline. For these projects, it is crucial to ensure there are effective processes in place. One way to ensure project success is to improve the manual processes of the usability inspection via automation. An automated usability tool will enable the evaluator to reduce manual processes and focus on capturing more defects in a shorter period of time. Thus improving the effectiveness of the usability inspection and minimizing defects escapee. There exist many usability testing and inspection methods. The scope of this paper is on the Heuristic Evaluation (HE) procedures automation. The Usability Management System (UMS) was developed to automate as many manual steps as possible throughout the software development life cycle (SDLC). It is important for the various teams within the organization to understand the benefits of automation. The results show that with the help of automation more usability defects can be detected. Hence, enhancing the effectiveness of usability evaluation by an automated Heuristic Evaluation System is feasible.

Defect prediction is an important aspect of the Product Development Life Cycle. The rationale in knowing predicted number of functional defects earlier on in the lifecycle, rather than to just find as many defects as possible during testing phase is to determine when to stop testing and ensure all the in-phase defects have been found in-phase before a product is delivered to the intended end user. It also ensures that wider test coverage is put in place to discover the predicted defects. This research is aimed to achieve zero known post release defects of the software delivered to the end user by MIMOS Berhad. To achieve the target, the research effort focuses on establishing a test defect prediction model using Design for Six Sigma methodology in a controlled environment where all the factors contributing to the defects of the product is within MIMOS Berhad. It identifies the requirements for the prediction model and how the model can benefit them. It also outlines the possible predictors associated with defect discovery in the testing phase. Analysis of the repeatability and capability of test engineers in finding defects are demonstrated. This research also describes the process of identifying characteristics of data that need to be collected and how to obtain them. Relationship of customer needs with the technical requirements of the proposed model is then clearly analyzed and explained. Finally, the proposed test defect prediction model is demonstrated via multiple regression analysis. This is achieved by incorporating testing metrics and development-related metrics as the predictors. The achievement of the whole research effort is described at the end of this study together with challenges faced and recommendation for future research work.

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