Towards Context-Aware Spectrum-Based Fault Localization

Many Spectrum-based Fault Localization (SBFL) techniques have been published over the years, but they are still rarely used in the industry. Traditional SBFL methods may be effective in certain situations but may fall short in others due to a lack of contextual information for the algorithm to function properly. I have made efforts to improve SBFL algorithms by investigating how SBFL aligns bug-fix patterns with high-ranked elements, creating an improved algorithm that uses call frequencies, examining how division by zero affects SBFL's efficiency, and proposing ways to avoid it. I have also developed a user feedback-based SBFL tool called CharmFL, which is helpful for Python programmers. I consider all of these things as contexts that can be used to create a more context-aware SBFL approach. In this doctoral symposium, I present my research and outline my plans for the remainder of my Ph.D. program.