Background: Any software project dataset sometimes includes outliers which affect the accuracy of effort estimation. Outlier deletion methods are often used to eliminate them. However, there are few case studies which apply outlier deletion methods to ...

Background: The prediction performance of a case-based reasoning (CBR) model is influenced by the combination of the following parameters: (i) similarity function, (ii) number of nearest neighbor cases, (iii) weighting technique used for attributes, and (...

Background: 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 ...

Background: Continuously calibrated and validated parametric models are necessary for realistic software estimates. However, in practice, variations in model adoption and usage patterns introduce a great deal of local bias in the resultant historical ...

Background. The bug assignment problem is the problem of triaging new bug reports to the most qualified developer. The qualified developer is the one who has enough knowledge in a specific area that is relevant to the reported bug. In recent years, bug ...

Background: Bug fixing lies at the core of most software maintenance efforts. Most prior studies examine the effort needed to fix a bug (fix-effort). However, the effort needed to fix a bug may not correlate with the calendar time needed to fix it (fix-...

Background: Software effort estimation (SEE) is a task of strategic importance in software management. Recently, some studies have attempted to use ensembles of learning machines for this task.

Aims: We aim at (1) evaluating whether readily available ...

Background: It is widely recognized that software effort estimation is a regression problem. Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning ...

Background: The past years have seen a surge of techniques predicting failure-prone locations based on more or less complex metrics. Few of these metrics are actionable, though.

Aims: This paper explores a simple, easy-to-implement method to predict and ...

Background: Missing data, which usually appears in software effort datasets, is becoming an important problem in software effort prediction.

Aims: In this paper, we adapt naïve Bayes and EM (Expectation Maximization) for software effort prediction, and ...

Background: Software engineering practices have evolved considerably over the last four decades, changing the way software systems are developed and delivered. Such evolvement may result in improvements in software productivity and changes in factors ...