In this experience paper, we share our experience on enhancing automatic unit test generation to more effectively find Java null pointer exceptions (NPEs). NPEs are among the most common and critical errors in Java applications. However, as we demonstrate in this paper, existing unit test generation tools such as Randoop and EvoSuite are not sufficiently effective at catching NPEs. Specifically, their primary strategy of achieving high code coverage does not necessarily result in triggering diverse NPEs in practice. In this paper, we detail our observation on the limitations of current state-of-the-art unit testing tools in terms of NPE detection and introduce a new strategy to improve their effectiveness. Our strategy utilizes both static and dynamic analyses to guide the test case generator to focus specifically on scenarios that are likely to trigger NPEs. We implemented this strategy on top of EvoSuite, and evaluated our tool, NpeTest, on 108 NPE benchmarks collected from 96 real-world projects. The results show that our NPE-guidance strategy can increase EvoSuite's reproduction rate of the NPEs from 56.9% to 78.9%, a 38.7% improvement. Furthermore, NpeTest successfully detected 89 previously unknown NPEs from an industry project.

We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.