SEAug 3, 2017

Testing as an Investment

arXiv:1708.00992v15 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need to justify testing decisions to non-technical stakeholders in engineering projects, though it is incremental by applying an existing financial model to a new domain.

The paper tackles the problem of evaluating software testing criteria from a business investment perspective by applying the Nelson-Siegel model to forecast short-term, medium-term, and long-term returns. Results show that statement-coverage criterion performs best for long-term yields, while short-term and medium-term yields depend on program scale and fault count.

Software testing is an expensive and important task. Plenty of researches and industrial efforts have been invested on improving software testing techniques, including criteria, tools, etc. These studies can provide guidelines to select suitable test techniques for software engineers. However, in some engineering projects, business issues may be more important than technical ones, hence we need to lobby non-technical members to support our decisions. In this paper, a well-known investment model, Nelson-Siegel model, is introduced to evaluate and forecast the processes of testing with different testing criteria. Through this model, we provide a new perspective to understand short-term, medium-term, and long-term returns of investments throughout the process of testing. A preliminary experiment is conducted to investigate three testing criteria from the viewpoint of investments. The results show that statement-coverage criterion performs best in gaining long-term yields; the short-term and medium-term yields of testing depend on the scale of programs and the number of faults they contain.

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