Compiler bugs are extremely harmful, but are notoriously difficult to debug because compiler bugs usually produce few debugging information. Given a bug-triggering test program for a compiler, hundreds of compiler files are usually involved during compilation, and thus are suspect buggy files. Although there are lots of automated bug isolation techniques, they are not applicable to compilers due to the scalability or effectiveness problem. To solve this problem, in this paper, we transform the compiler bug isolation problem into a search problem, i.e., searching for a set of effective witness test programs that are able to eliminate innocent compiler files from suspects. Based on this intuition, we propose an automated compiler bug isolation technique, DiWi, which (1) proposes a heuristic-based search strategy to generate such a set of effective witness test programs via applying our designed witnessing mutation rules to the given failing test program, and (2) compares their coverage to isolate bugs following the practice of spectrum-based bug isolation. The experimental results on 90 real bugs from popular GCC and LLVM compilers show that DiWi effectively isolates 66.67%/78.89% bugs within Top-10/Top-20 compiler files, significantly outperforming state-of-the-art bug isolation techniques.

During software evolution, both production code and test cases evolve frequently. To assure software quality, test cases should evolve in time. However, test case evolution is usually delayed and error-prone. To facilitate this process, this paper proposes a context-aware test case adaptation approach (CAT), which first identifies and generalizes test case adaptation patterns, and then applies these patterns to automatically evolve test cases by analyzing their context. We conducted a preliminary study on three open-source projects and found that CAT correctly adapts 71.91% of test cases.