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In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.

Software inspection is a proven method that enables the detection and removal of defects in software artifacts as soon as these artifacts are created. It usually involves activities in which a team of qualified personnel determines whether the created artifact is of sufficient quality. Detected quality deficiencies are subsequently corrected. In this way, an inspection cannot only contribute towards software quality improvement, but also lead to significant budget and time benefits. These advantages have already been demonstrated in many software development projects and organizations.

After Fagan's seminal paper presented in 1976, the body of work in software inspection has greatly increased and matured. This survey is to provide an overview of the large body of contributions in the form of incremental improvements and/or new methodologies that have been proposed to leverage and amplify the benefits of inspections within software development and even maintenance projects. To structure this large volume of work, it introduces, as a first step, the core concepts and relationships that together embody the field of software inspection. In a second step, the survey discusses the inspection-related work in the context of the presented taxonomy.

The survey is beneficial for researchers as well as practitioners. Researchers can use the presented survey taxonomy to evaluate existing work in this field and identify new research areas. Practitioners, on the other hand, get information on the reported benefits of inspections. Moreover, they find an explanation of the various methodological variations and get guidance on how to instantiate the various taxonomy dimensions for the purpose of tailoring and performing inspections in their software projects.