Generic repair of complex data structures is a new and exciting area of research. Existing approaches can integrate with good software engineering practices such as program assertions. But in practice there is a wide variety of assertions and not all of them satisfy the style rules imposed by existing repair techniques. I.e., a "badly" written assertion may render generic repair inefficient or ineffective. In this paper we build on the state of the art in generic repair and discuss how generic repair can work effectively with a wider range of correctness conditions.We motivate how dynamic symbolic techniques enable generic repair to support a wider range of correctness conditions and present DSDSR, a novel repair algorithm based on dynamic symbolic execution. We implement the algorithm for Java and report initial empirical results to demonstrate the promise of our approach for generic repair.

No abstract

Summary Benchmarks are heavily used in different areas of computer science to evaluate algorithms and tools. In program analysis and testing, open‐source and commercial programs are routinely used as benchmarks to evaluate different aspects of algorithms and tools. Unfortunately, many of these programs are written by programmers who introduce different biases, not to mention that it is very difficult to find programs that can serve as benchmarks with high reproducibility of results. We propose a novel approach for generating random benchmarks for evaluating program analysis and testing tools and compilers. Our approach uses stochastic parse trees, where language grammar production rules are assigned probabilities that specify the frequencies with which instantiations of these rules will appear in the generated programs. We implemented our tool for Java and applied it to generate a set of large benchmark programs of up to 5M lines of code each with which we evaluated different program analysis and testing tools and compilers. The generated benchmarks let us independently rediscover several issues in the evaluated tools. Copyright © 2014 John Wiley & Sons, Ltd.

No abstract

Benchmarks are heavily used in different areas of computer science to evaluate algorithms and tools. In program analysis and testing, open-source and commercial programs are routinely used as benchmarks to evaluate different aspects of algorithms and tools. Unfortunately, many of these programs are written by programmers who introduce different biases, not to mention that it is very difficult to find programs that can serve as benchmarks with high reproducibility of results.We propose a novel approach for generating random benchmarks for evaluating program analysis and testing tools. Our approach uses stochastic parse trees, where language grammar production rules are assigned probabilities that specify the frequencies with which instantiations of these rules will appear in the generated programs. We implemented our tool for Java and applied it to generate benchmarks with which we evaluated different program analysis and testing tools. Our tool was also implemented by a major software company for C++ and used by a team of developers to generate benchmarks that enabled them to reproduce a bug in less than four hours.