Predicting fault-prone modules with case-based reasoning

Khoshgoftaar, Taghi M.; Ganesan, K.; Allen, Edward B.; Ross, Fletcher D.; Munikoti, Rama; Goel, Nishith; Nandi, Amit

doi:10.1016/s0920-5489(99)92194-3

Cited by 7 publications

(8 citation statements)

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“…The basic hypothesis of software quality prediction is that a module currently under development is fault prone if a module with the similar product or process metrics in an earlier project (or release) developed in the same environment was fault prone [30]. Therefore, the information available early within the current project or from the previous project can be used in making predictions.…”

Section: E Testingmentioning

confidence: 99%

New Trends in Learning for Software Engineering

Hamouda¹

2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance.

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Section: E Testingmentioning

confidence: 99%

New Trends in Learning for Software Engineering

Hamouda¹

2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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“…In defect prediction literature, there are many defect prediction algorithms studied like regression [43] [10] [40], rule induction [40], decision tree approaches like C4.5 [42], case-based reasoning (CBR) [23] [22] [40], artificial neural networks [24] [44] [21] [40], linear discriminant analysis [31], k-nearest neighbour [6], k-star [25], Bayesian networks [12] [35] [46] and support vector machine based classifiers [26] [19] [20] [41]. According to the no free lunch theorem [45], there is no algorithm which is better than other algorithms on all data sets.…”

Section: Introductionmentioning

confidence: 99%

Software defect prediction using Bayesian networks

2012

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There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.

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“…The basic hypothesis of software quality prediction is that a module currently under development has defects if a module with the similar product or process metrics in an earlier project (or release) developed in the same environment had defects [7] . Therefore, the information available early within the current project or from the previous project can be used in making predictions.…”

Section: Introductionmentioning

confidence: 99%

Intelligence System for Software Maintenance Severity Prediction

Sandhu¹,

Kumar²,

Singh³

2007

J. of Computer Science

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The software industry has been experiencing a software crisis, a difficulty of delivering software within budget, on time, and of good quality. This may happen due to number of defects present in the different modules of the project that may require maintenance. This necessitates the need of predicting maintenance urgency of the particular module in the software. In this paper, we have applied the different predictor models to NASA five public domain defect datasets coded in C, C++, Java and Perl programming languages. Twenty one software metrics of different datasets and Java Classes of thirty five algorithms belonging to the different learner categories of the WEKA project have been evaluated for the prediction of maintenance severity. The results of ten fold cross validation are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for different project datasets. The results show that logistic model Trees (LMT) and Complimentary Naïve Bayes (CNB) based Model provide a relatively better prediction consistency compared to other models and hence, can be used for the maintenance severity prediction of the software. The developed system can also be used for analysis and to evaluate the influence of different factors on the maintenance severity of different software project modules

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Predicting fault-prone modules with case-based reasoning

Cited by 7 publications

References 0 publications

New Trends in Learning for Software Engineering

New Trends in Learning for Software Engineering

Software defect prediction using Bayesian networks

Intelligence System for Software Maintenance Severity Prediction

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