lmbench: Portable tools for performance analysis lmbench is a micro-benchmark suite designed to focus attention on the basic building blocks of many common system applications, such as databases, simulations, software development, and networking. In almost all cases, the individual tests

ABSTRACT Software testing can be re-formulated as a search problem, hence search algorithms (e.g., Genetic Algorithms) can be used to tackle it. Most of the research so far has been of empirical nature, in which novel proposed techniques have been validated on software testing benchmarks. However

little attention in literature. In this paper, we analyse the role that the length plays in software testing. We show that on “difficult ” software testing benchmarks longer test sequences make their testing trivial. Hence, we argue that the choice of the length of the test sequences is very important

-directed compilation, and software test coverage. This paper describes a new algorithm for path profiling. This simple, fast algorithm selects and places profile instrumentation to minimize run-time overhead. Instrumented programs run with overhead comparable to the best previous profiling techniques. On the SPEC95

Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This paper presents

a quantitative study of faults and failures in two releases of a major commercial system. We tested a range of basic software engineering hypotheses relating to: the Pareto principle of distribution of faults and failures; the use of early fault data to predict later fault and failure data; metrics

-determinism in interrupt delivery complements well with statistical processing. We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose

Background: Software agents are becoming increasingly common in the engineering of software systems. We explore the use of humans in creating benchmarks for the evaluation of software agents. In our case studies, we address the domain of instructable software agents (e-students) as proposed

a mismatch tree data structure, allowing us to calculate the contributions of all patterns occurring in the data in one pass while traversing the tree. When used with an SVM, the kernels enable fast prediction on test sequences. We report experiments on two benchmark SCOP datasets, where we show

Multithreaded programs are difficult to get right because of unexpected interaction between concurrently executing threads. Traditional testing methods are inadequate for catching subtle concurrency errors which manifest themselves late in the development cycle and post-deployment. Model checking