As software evolves, becoming a more integral part of complex systems, modern society becomes more reliant on the proper functioning of such systems. However, the field of software quality assurance lacks detailed empirical studies from which best practices can be determined. The fundamental factors that contribute to software quality are faults, failures and fixes, and although some studies have considered specific aspects of each, comprehensive studies have been quite rare. Thus, the fact that we establish the cause-effect relationship between the fault(s) that caused individual failures, as well as the link to the fixes made to prevent the failures from (re)occurring appears to be a unique characteristic of our work. In particular, we analyze fault types, verification activities, severity levels, investigation effort, artifacts fixed, components fixed, and the effort required to implement fixes for a large industrial case study. The analysis includes descriptive statistics, statistical inference through formal hypothesis testing, and data mining. Some of the most interesting empirical results include (1) Contrary to popular belief, later life-cycle faults dominate as causes of failures. Furthermore, over 50% of high priority failures (e.g., post-release failures and safety-critical failures) were caused by coding faults. (2) 15% of failures led to fixes spread across multiple components and the spread was largely affected by the software architecture. (3) The amount of effort spent fixing faults associated with each failure was not uniformly distributed across failures; fixes with a greater spread across components and artifacts, required more effort. Overall, the work indicates that fault prevention and elimination efforts focused on later life cycle faults is essential as coding faults were the dominating cause of safety-critical failures and post-release failures. Further, statistical correlation and/or traditional data mining techniques show potential for assessment and prediction of the locations of fixes and the associated effort. By providing quantitative results and including statistical hypothesis testing, which is not yet a standard practice in software engineering, our work enriches the empirical knowledge needed to improve the state-of-the-art and practice in software quality assurance.
Software Quality Assurance For Software Engineers
