The ability to dynamically adapt to resource variations is critical for modern-day mission-critical systems that operate in ever-changing resource environments. Test-based Software Modification (TBSM) is a recently proposed technique to build Resource Adaptive Software (RAS) that relies on existing test infrastructure, test labeling, and program modifications. TBSM is simple and applicable, but an inefficient technique; the primary reason for inefficiency is the sheer size of the search space. In this paper, we propose AdFL, a repurposing of Fault Localization (FL) that can shrink (and prioritize) the search space for TBSM more effectively than previously proposed heuristics. We present complete case studies and an empirical analysis of a set of open source projects as evidence that AdFL can significantly reduce the search space in TBSM. We show how to combine AdFL with previous heuristics for TBSM, and propose an incremental, best-effort variant of TBSM that uses AdFL to prioritize the search.

Building software systems that adapt to changing resources is challenging: developers cannot anticipate all future situations that a software system may face, and even if they could, the effort required would be onerous. A conceptually simple, yet practically applicable, way to build resource adaptive software is to use test-based software minimization, where tests define functionality. One drawback of the approach is that it requires a time-consuming reduction process that removes program statements in order to reduce resource usage, making it impractical for use in deployed systems. We show that statements removed have predictable characteristics, making it possible to use heuristics to choose statements to analyze. We demonstrate the utility of our heuristics via a case study of the NetBeans IDE: using our best heuristic, we were able to compute an effective resource adaptation almost 3 times faster than without heuristic guidance.

Random testing can be a powerful and scalable method for finding faults in software. However, sophisticated random testers usually test a whole program, not individual components. Writing random testers for individual components of complex programs may require unreasonable effort. In this paper we present a novel method, directed swarm testing, that uses statistics and a variation of random testing to produce random tests that focus on only part of a program, increasing the frequency with which tests cover the targeted code. We demonstrate the effectiveness of this technique using real-world programs and test systems (the YAFFS2 file system, GCC, and Mozilla's SpiderMonkey JavaScript engine), and discuss various strategies for directed swarm testing. The best strategies can improve coverage frequency for targeted code by a factor ranging from 1.1-4.5x on average, and from nearly 3x to nearly 9x in the best case. For YAFFS2, directed swarm testing never decreased coverage, and for GCC and SpiderMonkey coverage increased for over 99% and 73% of targets, respectively, using the best strategies. Directed swarm testing improves detection rates for real SpiderMonkey faults, when the code in the introducing commit is targeted. This lightweight technique is applicable to existing industrial-strength random testers.