An important step in predicting error prone modules in a project is to construct the prediction model by using training data of that project, but the resulting prediction model depends on the training data. Therefore it is difficult to apply the model to other projects. The training data consists of metrics data and bug data, and these data should be prepared for each project. Metrics data can be computed by using metric tools, but it is not so easy to collect bug data. In this paper, we try to reuse the generated prediction model. By using the metrics and bug data which are computed from C++ and Java projects, we have evaluated the possibility of applying the prediction model, which is generated based on one project, to other projects, and have proposed compensation techniques for applying to other projects. We showed the evaluation result based on open source projects.

It is difficult to identify whether quality requirements are defined adequately or not, but there are few methods to support this kind of requirements analysis. In this paper, we propose a method based on software quality spectrum, that shows a ratio of quality characteristics embedded in a software engineering artifact, such as a requirements specification, a manual and so on. We assume similar kinds of software systems have similar spectrum, thus we can identify the adequacy of quality requirements for a new system by using spectrum of already existing similar systems. We confirmed the assumption above by analyzing actual software systems, i.e., web browsers and drawing tools.

SUMMARYQuality requirements are scattered over a requirements specification, thus it is hard to measure and trace such quality requirements to validate the specification against stakeholders' needs. We proposed a technique called "spectrum analysis for quality requirements" which enabled analysts to sort a requirements specification to measure and track quality requirements in the specification. In the same way as a spectrum in optics, a quality spectrum of a specification shows a quantitative feature of the specification with respect to quality. Therefore, we can compare a specification of a system to another one with respect to quality. As a result, we can validate such a specification because we can check whether the specification has common quality features and know its specific features against specifications of existing similar systems. However, our first spectrum analysis for quality requirements required a lot of effort and knowledge of a problem domain and it was hard to reuse such knowledge to reduce the effort. We thus introduce domain knowledge called term-characteristic map (TCM) to reuse the knowledge for our quality spectrum analysis. Through several experiments, we evaluate our spectrum analysis, and main finding are as follows. First, we confirmed specifications of similar systems have similar quality spectra. Second, results of spectrum analysis using TCM are objective, i.e., different analysts can generate almost the same spectra when they analyze the same specification.

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