Software defect prediction based on association rule classification.

In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been investigated so far in this context. This paper assesses the use of such a classification method, CBA2, and compares it to other rule based classification methods. Furthermore, we investigate whether rule sets generated on data from one software project can be used to predict defective software modules in other, similar software projects. It is found that applying the CBA2 algorithm results in both accurate and comprehensible rule sets.Software defect prediction; Association rule classification; CBA2; AUC;

Essays on empirical software engineering.

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Software requirements engineering: een iteratieve aanpak

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Towards comprehensible software fault prediction models using Bayesian network classifiers

Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility to construct simpler networks with less nodes and arcs remain unexplored. This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Demsar. It is concluded that simple and comprehensible networks with less nodes can be constructed using BN classifiers other than the Naive Bayes classifier. Furthermore, it is found that the aspects of comprehensibility and predictive performance need to be balanced out, and also the development context is an item which should be taken into account during model selection

A multidimensional analysis of data quality for credit risk management: new insights and challenges

Interest in group moods as an emergent phenomenon of group members’ interactions has significantly increased over the past two decades (Barsade & Gibson, 2007). Most studies focused particularly on understanding the effects of group moods on group processes (Barsade, 2001, Baartel & Saavedra, 2000, Barsade, Ward, Turner & Sonnenfled, 2000, Chiayu Tu, 2009) and group performance (Seung -Yoon Ree, 2006, Jordan, Lawrence & Troth, 2006). However, research investigating the antecedents of group moods is still scant. The current study fills this gap by focusing on the affective potential of group conflict. In this sense, group conflict focuses on how differences of opinion (task conflict) and person-related disagreements (relationship conflict) trigger group moods that differ in their valence (positive and negative) and level of activation (activated and unactivated) (Baartel & Saavedra, 2000). In this context, the group’s ability to define and understand its moods, their cause, evolution and relations between them - ability known as group emotional intelligence (Salovey & Mayer, 1990) - is expected to buffer the relation between conflict and group moods. By studying group moods in relation to group conflict, the current study extends previous research by considering group moods’ antecedents and not only their consequences. This contributes to a better understanding of group affect dynamics. In addition, the current study investigates different nuances of group moods given by different types of conflict. Whether an affect has a positive or negative valence, or whether it is activated or inactivated, has implications upon the further group dynamics

Data mining techniques for software effort estimation: a comparative study

A predictive model is required to be accurate and comprehensible in order to inspire confidence in a business setting. Both aspects have been assessed in a software effort estimation setting by previous studies. However, no univocal conclusion as to which technique is the most suited has been reached. This study addresses this issue by reporting on the results of a large scale benchmarking study. Different types of techniques are under consideration, including techniques inducing tree/rule based models like M5 and CART, linear models such as various types of linear regression, nonlinear models (MARS, multilayered perceptron neural networks, radial basis function networks, and least squares support vector machines), and estimation techniques that do not explicitly induce a model (e.g., a case-based reasoning approach). Furthermore, the aspect of feature subset selection by using a generic backward input selection wrapper is investigated. The results are subjected to rigorous statistical testing and indicate that ordinary least squares regression in combination with a logarithmic transformation performs best. Another key finding is that by selecting a subset of highly predictive attributes such as project size, development, and environment related attributes, typically a significant increase in estimation accuracy can be obtaine

Software defect prediction based on association rule classification

In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been investigated so far in this context. This paper assesses the use of such a classification method, CBA2, and compares it to other rule based classification methods. Furthermore, we investigate whether rule sets generated on data from one software project can be used to predict defective software modules in other, similar software projects. It is found that applying the CBA2 algorithm results in both accurate and comprehensible rule sets.nrpages: 8status: publishe

De kosten van software-ontwikkeling voorspellen.

Om de kosten van softwareontwikkeling in te schatten kunnen dataminingtechnieken worden gebruikt. Recentelijk zijn de formele modellen op de achtergrond geraakt ten gunste van de - objectievere - dataminingtechnieken. Uit onderzoek naar het gebruik van technieken als regressie, neurale netwerken en beslissingsbomen op vier softwareontwikkeldatasets blijkt dat de eenvoudige technieken het beste scoren.