Software Defect Prediction Using Regression via Classification

Bibi, Stamatia; Tsoumakas, Grigorios; Stamelos, Ioannis; Vlahvas, I.

doi:10.1109/aiccsa.2006.205110

Cited by 42 publications

(32 citation statements)

References 19 publications

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“…The relationship is expressed in the form of an equation that predicts the response variable as a linear function of predictor variable. [42,24,51,25] Linear Regression: Y=a+bX+u 2. Association Rule Mining: It is a method for discovering interesting relationships between variables in large databases.…”

Section: Software Defect Predictionmentioning

confidence: 99%

A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Prasad¹,

Florence²,

Arya³

2015

IJDTA

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Section: Software Defect Predictionmentioning

confidence: 99%

A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Prasad¹,

Florence²,

Arya³

2015

IJDTA

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“…Many studies in defect prediction have been reported using techniques which originated from the field of statistics and machine learning. Such techniques include regression [8], logistic regression [18,75], Support Vector Machines [20], etc. Others have their origin in machine learning techniques such as classification trees [34]), neural networks [35], probabilistic techniques (such as Naïve Bayes [53] and Bayesian networks [24]), Case Based Reasoning ( [36]), ensembles of different techniques and meta-heuristic techniques such as ant colony optimisation [33,5,67], etc.…”

Section: Related Workmentioning

confidence: 99%

“…The AR dataset was obtained by combining several datasets (AR1, AR3, AR4, AR5, AR6) all from embedded systems developed in C by a Turkish white-goods manufacturer as the individual datasets were very small. In this case, the metrics were obtained using the PREST tool 8 . Although both the NASA and the AR datasets share the same metrics, we did not merge them as they were collected using different tools and belong to different domains.…”

Section: Datasetsmentioning

confidence: 99%

A study of subgroup discovery approaches for defect prediction

Rodríguez

Ruíz

Riquelme

et al. 2013

Information and Software Technology

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Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the precise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with publicly available datasets from the Promise repository and object-oriented metrics from an Eclipse repository related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocessing techniques. * Corresponding author Email addresses: daniel.rodriguezg@uah.es (Daniel Rodriguez), robertoruiz@upo.es (Roberto Ruiz), riquelme@lsi.us.es (Jose C. Riquelme), rachel.harrison@brookes.ac.uk (Rachel Harrison) Preprint submitted to IST May 5, 2013 Results: The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules. Conclusions:The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.

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“…Bibi et al [15] performed their research on estimating the number of defects through regression via classification. In their model, 5 classification variables, 10 quantitative variables and 10 risk factors were adopted as predictors.…”

Section: Related Workmentioning

confidence: 99%