Application of neural networks to software quality modeling of a very large telecommunications system

Khoshgoftaar, Taghi M.; Allen, Edward B.; Hudepohl, J.P.; Aud, S.J.

doi:10.1109/72.595888

Cited by 172 publications

(80 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first publication related to an industrial application of defect prediction was published in 1997 by Khoshgoftaar et al [13]. It was a case study of quality modeling for a very large telecommunications system.…”

Section: Related Workmentioning

confidence: 99%

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Hryszko

Madeyski

2018

Foundations of Computing and Decision Sciences

View full text Add to dashboard Cite

Abstract. Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.

show abstract

Section: Related Workmentioning

confidence: 99%

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Hryszko

Madeyski

2018

Foundations of Computing and Decision Sciences

View full text Add to dashboard Cite

show abstract

“…Khoshgoftaar et al [26] described the use of neural networks for quality prediction. The authors used a dataset from a telecommunications system and compare the neural networks results with a non-parametric model.…”

Section: Related Workmentioning

confidence: 99%

Searching for rules to detect defective modules: A subgroup discovery approach

Rodríguez

Ruíz

Riquelme

et al. 2012

Information Sciences

View full text Add to dashboard Cite

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only faultprone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository * Corresponding author Email addresses: daniel.rodriguezg@uah.es (D. Rodríguez), robertoruiz@upo.es (R. Ruiz), riquelme@lsi.us.es (J.C. Riquelme), aguilar@upo.es (J.S. Aguilar-Ruiz) February 4, 2011 showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases. Preprint submitted to Information Sciences

show abstract

“…For example, Xiao Yu propose a novel probabilistic graphical model called Bayesian Network based Program Dependence Graph (BNPDG) to locate the defect in the software. In addition, decision trees, neural networks and other methods have also been widely used to predict the defect of software [23][24].…”

Section: Related Workmentioning

confidence: 99%

Is the Number of Faults Helpful for Cross-Company Defect Prediction?

Jing¹,

Zhang²,

Jin³

2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-In the field of Cross-company Defect Prediction (CCDP), how to deal with the data to make it more accurately predict the cross-company software defects is the focus problem we need to consider. Now a mainstream idea is to determine the weight of the training data based on the similarity between the training data and the test set, and then build the model on the basis of these weighted data. However, sometimes, when we deal with some problems with imbalance class, directly using the weight calculated above may lead to errors. Because a large number of non-defective instance's weight accumulation will lead to the defective instance's weight has a little impact on the final result. This is why we need to consider the addition of the number of defects. Considering the number of defects will effectively eliminate the impact of a large number of non-defective instance's weight accumulation. Therefore, we propose a Transfer-learning Naï ve Bayes model considering the number of defective information(NTNB). The method consists of two major stages: weight the data and build the prediction model. In the stage of weighting the data, we not only consider the similarity between the data but also consider the number of defects to get the final weights for data. And we conducted a set of comparative experiments on six open crosscompany datasets. The results show that considering the number of defects information can effectively avoid some defective instance is misjudged as non-defective instance, and improve the accuracy of prediction in some unbalanced problems.

show abstract

Application of neural networks to software quality modeling of a very large telecommunications system

Cited by 172 publications

References 32 publications

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Searching for rules to detect defective modules: A subgroup discovery approach

Is the Number of Faults Helpful for Cross-Company Defect Prediction?

Contact Info

Product

Resources

About