Improving test models for large scale industrial systems: an inquisitive study
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
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Testing is the most common way to increase confidence in the correctness and reliability of software. Studies report that testing consumes about half the cost of software development. Studies also show that maintenance can consume up to 80% of the cost for the entire software lifecycle, and much of that cost is devoted to testing. Rapidly changing software and computing environments present many challenges for effective and efficient testing in practice. Past research in testing of evolving software has resulted in techniques that attempt to automate or partially automate the process. Although few of these techniques have been successfully transferred to practice, existing techniques show promise for use in industry. By combining program analysis, machine learning, and visualization techniques, we can expect significant improvement in the process of testing evolving software that will provide reduction in cost and improvement in quality.