Toward improving software testing

As today's software overgrows in size and complexity, software reliability prediction plays a crucial role in software development. Reliability is a broad concept, and it is applied whenever we expect something to behave in a certain way.

Many software disasters such as NASA Mars Climate Orbiter in 1999, European Space Agency's Ariane 5 rocket in 1996, Northeastern united states and Ontario Canada blackout in 2003, Overexposure of radiation therapy patients in National Cancer Institute of Panama City in 2000 and may more, happened due to lack of proper testing.

Therefore, during the third Lunchtime Talk Series powered by PechaKucha on 29 September 2021, Dr Golnoush Abaei from the School of Information Technology highlighted that software testing is an essential and expensive task during the software development process, which guarantees software quality. It has the potential to save time and budget by recognising faults as early as possible.

A software company uses fault prediction when it needs to deliver a finished product. At the same time, it has limited time and budget for testing it as one of the methods to improve the testing process. Fault prediction approaches identify parts of the system that are more defect prone. However, many issues need to be considered while building a prediction model. Sometimes, traditional models are not good choices for creating a suitable prediction model. Things like when the defect data or quality-based class labels are not identified, when a company does not have similar or earlier versions of the software project, or when the data is highly imbalanced are some of the issues that need to be addressed when building a prediction model.

Another method to improve the testing process while reducing testing time and expenses are using test case prioritisation and selection techniques. Only selected or high prioritised ones are executed, which is beneficial for delivering a high-quality product, especially when a project is behind schedule and over budget.