The first international Worst Case Execution Time (WCET) Tool Challenge in 2006 used benchmark programs to evaluate academic and commercial WCET tools. It aimed to study the state-of-the-art in WCET analysis. The WCET Tool Challenge comprised two parallel evaluation approaches: an internal evaluation by the respective tool developers and an external test by a neutral person of an independent institute. The latter was conducted by the author of this paper. Focusing on the external test, we describe the rules, benchmarks, participants and discuss the obtained results.
The first Worst Case Execution Time (WCET) Tool Challenge, performed in 2006, attempted to evaluate the state-ofthe-art in timing analysis for real-time systems and so to encourage research and activities in the WCET community. It applied two evaluation approaches to the tools submitted, selfevaluation and external evaluation. In order to balance users' effects and to achieve a comparable evaluation, an independent test person has been assigned by the WCET Challenge Working Group to perform the external tool evaluation. The test person visited the tool developers and evaluated the tools entered. This paper describes the testing procedures applied, the results obtained, and the evaluation made in the Challenge.
A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12.
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