Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average. 1

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper 1 provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

paradigm that uses automated search algorithms to help designers iteratively find solutions to complicated design problems. For example, when designing a climate monitoring satellite, designers may want to use the minimal amount of computing hardware to reduce weight and cost, while supporting the image processing algorithms running onboard. A key problem in these situations is that the hardware and software design are locked in a tightly-coupled cost-constrained producer/consumer relationship that makes it hard to find a good hardware/software design configuration. Search-based software engineering can be used to apply algorithmic techniques to automate the search for hardware/software designs that maximize the image processing accuracy while respecting cost constraints. This paper provides the following contributions to research on searchbased software engineering: (1) we show how a cost-constrained producer/consumer problem can be modeled as a set of two multidimensional multiple-choice knapsack problems (MMKPs), (2) we present a polynomial-time search-based software engineering technique, called the Allocation-baSed Configuration Exploration Technique (ASCENT), for finding near optimal hardware/software co-design solutions, and (3) we present empirical results showing that ASCENT’s solutions average 95%+ of the optimal solution’s value. 1

Abstract—This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totaling 1.25 M lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyze the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorizations, and do not lead to serious degradation in functionality. Index Terms—Automatic programming, corrections, testing and debugging. Ç 1