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Abstract-Debugging consumes significant time and effort in any major software development project. Moreover, even after the root cause of a bug is identified, fixing the bug is non-trivial. Given this situation, automated program repair methods are of value. In this paper, we present an automated repair method based on symbolic execution, constraint solving and program synthesis. In our approach, the requirement on the repaired code to pass a given set of tests is formulated as a constraint. Such a constraint is then solved by iterating over a layered space of repair expressions, layered by the complexity of the repair code. We compare our method with recently proposed genetic programming based repair on SIR programs with seeded bugs, as well as fragments of GNU Coreutils with real bugs. On these subjects, our approach reports a higher success-rate than genetic programming based repair, and produces a repair faster.

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Over the recent years, the popularity of smartphones has increased dramatically. This has lead to a widespread availability of smartphone applications. Since smartphones operate on a limited amount of battery power, it is important to develop tools and techniques that aid in energy-efficient application development. Energy inefficiencies in smartphone applications can broadly be categorized into energy hotspots and energy bugs. An energy hotspot can be described as a scenario where executing an application causes the smartphone to consume abnormally high amount of battery power, even though the utilization of its hardware resources is low. In contrast, an energy bug can be described as a scenario where a malfunctioning application prevents the smartphone from becoming idle, even after it has completed execution and there is no user activity.In this paper, we present an automated test generation framework that detects energy hotspots/bugs in Android applications. Our framework systematically generates test inputs that are likely to capture energy hotspots/bugs. Each test input captures a sequence of user interactions (e.g. touches or taps on the smartphone screen) that leads to an energy hotspot/bug in the application. Evaluation with 30 freely-available Android applications from Google Play/F-Droid shows the efficacy of our framework in finding hotspots/bugs. Manual validation of the experimental results shows that our framework reports reasonably low number of false positives. Finally, we show the usage of the generated results by improving the energy-efficiency of some Android applications.

review articles ALEX IS A software developer, a recent hire at the company of her dreams. She is finally ready to push a highly anticipated new feature to the shared code repository, an important milestone in her career as a developer. As is increasingly common in development practice, this kind of push triggers myriads of tests the code must pass before becoming visible to everyone in the company. Alex has heavily tested the new feature and is confident it will pass all the tests automatically triggered by the push. Unfortunately, Alex learns the build system rejected the commit. The continuous integration system reports failed tests associated with a software package developed by a different team entirely. Alex now must understand the problem and fix the feature manually. What if, instead of simply informing Alex of the failing test, the build system also suggested one or two possible patches for the committed code? Although this general use case is still fictional, a growing community

Estimating the Worst Case Execution Time (WCET) of real-time embedded software is an important problem. WCET is defined as the upper bound b on the execution time of a program P on a processor X such that for any input the execution time of P on X is guaranteed to not exceed b. Such WCET estimates are crucial for schedulability analysis of real-time systems. In this paper, we present Chronos, a static analysis tool for generating WCET estimates of C programs. It performs detailed micro-architectural modeling to capture the timing effects of the underlying processor platform. Consequently, we can provide safe but tight WCET estimate of a given C program running on a complex modern processor. Chronos is an open-source distribution specifically suited to the needs of the research community. We support processor models captured by the popular SimpleScalar architectural simulator rather than targeting specific commercial processors. This makes the Chronos flexible, extensible and easily accessible to the researcher.