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Accurate detection of fault prone modules offers the path to high quality software products while minimizing non essential assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. Establishing whether a module contains a fault or not can be expensive. The(More)
—Thanks to the ever increasing importance of project data, its collection has been one of the primary focuses of software organizations. Data collection activities have resulted in the availability of massive amounts of data through software data repositories. This is great news for the predictive modeling research in software engineering. However, widely(More)
<b><i>Background:</i></b> Developing and maintaining a software effort estimation (SEE) data set <i>within</i> a company (<i>within data</i>) is costly. Often times parts of data may be missing or too difficult to collect, e.g. effort values. However, information about the past projects-although incomplete- may be helpful, when incorporated with the SEE(More)
<b>Background:</b> Software quality prediction plays an important role in improving the quality of software systems. By mining software metrics, predictive models can be induced that provide software managers with insights into quality problems they need to tackle as effectively as possible. <b>Objective:</b> Traditional, supervised learning approaches(More)
This paper augments Boehm-Turner's model of agile and plan-based software development augmented with an AI search algorithm. The AI search finds the key factors that predict for the success of agile or traditional plan-based software developments. According to our simulations and AI search algorithm: (1) in no case did agile methods perform worse than(More)
<b><i>Background:</i></b> Many statistical and machine learning techniques have been implemented to build predictive fault models. Traditional methods are based on supervised learning. Software metrics for a module and corresponding fault information, available from previous projects, are used to train a fault prediction model. This approach calls for a(More)
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