Test-suite minimization techniques aim to eliminate redundant test cases from a test-suite based on some criteria, such as coverage or fault-detection capability. Most existing test-suite minimization techniques have two main limitations: they perform minimization based on a single criterion and produce suboptimal solutions. In this paper, we propose a test-suite minimization framework that overcomes these limitations by allowing testers to (1) easily encode a wide spectrum of test-suite minimization problems, (2) handle problems that involve any number of criteria, and (3) compute optimal solutions by leveraging modern integer linear programming solvers. We implemented our framework in a tool, called MINTS, that is freely-available and can be interfaced with a number of different state-of-the-art solvers. Our empirical evaluation shows that MINTS can be used to instantiate a number of different test-suite minimization problems and efficiently find an optimal solution for such problems using different solvers.

Most existing fault-localization techniques focus on identifying and reporting single statements that may contain a fault. Even in cases where a fault involves a single statement, it is generally hard to understand the fault by looking at that statement in isolation. Faults typically manifest themselves in a specific context, and knowing that context is necessary to diagnose and correct the fault. In this paper, we present a novel fault-localization technique that identifies sequences of statements that lead to a failure. The technique works by analyzing partial execution traces corresponding to failing executions and identifying common segments in these traces, incrementally. Our approach provides developers a context that is likely to result in a more directed approach to fault understanding and a lower overall cost for debugging.