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Collaboration between industry and academia supports improvement and innovation in industry and helps to ensure industrial relevance in academic research. This article presents an exploratory study of the factors for successful collaboration between industry and academia in software research.
Statistical fault prediction models and expert estimations are two popular methods for deciding where to focus the fault detection efforts when the fault detection budget is limited. In this paper, we present a study in which we empirically compare the accuracy of fault prediction offered by statistical prediction models with the accuracy of expert… (More)
Software systems are normally developed in a number of releases. Each release usually modifies existing code. In this study we show that such modified code can be an important source of faults. Since faults are considered major cost drivers of software projects, the ability to identify fault-prone classes before they are implemented would give a chance to… (More)
Fault prediction models still seem to be more popular in academia than in industry. In industry expert estimations of fault proneness are the most popular methods of deciding where to focus the fault detection efforts. In this paper we present a study in which we empirically evaluate the accuracy of fault prediction offered by statistical models as compared… (More)
In this paper we suggest and evaluate a method for predicting fault densities in modified classes early in the development process, i.e., before the modifications are implemented. We start by establishing methods that according to literature are considered the best for predicting fault densities of modified classes. We find that these methods can not be… (More)
Many software systems are developed in a number of consecutive releases. Each new release does not only add new code but also modifies already existing one. In this study we have shown that the modified code can be an important source of faults. The faults are widely recognized as one of the major cost drivers in software projects. Therefore we look for… (More)