Towards an ensemble based system for predicting the number of software faults

Rathore, Santosh Singh; Kumar, Sandeep

doi:10.1016/j.eswa.2017.04.014

Cited by 87 publications

(32 citation statements)

References 64 publications

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“…The obtained results show that RB2CBL outperform standard BR model. Rathore and Kumar [22] tackle two different datasets from PROMISE repository and Eclipse bug data repository using two ensemble learners methods, linear regression based combination rule (LRCR) and Gradient boosting regression based combination rule (GRCR). The authors employed two evaluation criteria evaluate the obtained results, Average Absolute Error (AAE) and Average Relative Error (ARE), and GRCR model outperforms LRCR one.…”

Section: A Software Fault Predictionmentioning

confidence: 99%

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

et al. 2020

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Software fault prediction (SFP) is a complex problem that meets developers in the software development life cycle. Collecting data from real software projects, either while the development life cycle or after lunch the product, is not a simple task, and the collected data may suffer from imbalance data distribution problem. In this research, we proposed an Enhanced Binary Moth Flame Optimization (EBMFO) with Adaptive synthetic sampling (ADASYN) to predict software faults. BMFO is employed as a wrapper feature selection, while ADASYN enhances the input dataset and address the imbalanced dataset. Converting MFO algorithm from a continues version to the binary version using transfer functions (TFs) from two different groups (S-shape and V-shape) is investigated in this work and proposed an EBFMFO version. Fifteen real projects data obtained from PROMISE repository are employed in this work. Three different classifiers are used: the k-nearest neighbors (k-NN), Decision Trees (DT), and Linear discriminant analysis (LDA). The reported results demonstrate that the proposed EBMFO enhances the overall performance of classifiers and outperforms the results in the literature and show the importance of TF for feature selection algorithms.

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Section: A Software Fault Predictionmentioning

confidence: 99%

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

et al. 2020

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“…Many previous studies have examined the capabilities of object‐oriented metrics for software fault prediction and found that object‐oriented metrics performed better than static code metrics in predicting software faults because metrics represent various structural characteristics of object‐oriented software systems like coupling, cohesion, inheritance, encapsulation, complexity, and size metrics …”

Section: Introductionmentioning

confidence: 99%

“…Many previous studies have examined the capabilities of object-oriented metrics for software fault prediction and found that object-oriented metrics performed better than static code metrics in predicting software faults because metrics represent various structural characteristics of object-oriented software systems like coupling, cohesion, inheritance, encapsulation, complexity, and size metrics. 3 Genetics algorithm (GA) is an optimization algorithm, which was first used by Holland in 1975. GA depends on selecting the most effective solution among wide space of solutions, so in order to execute GA, a primary population should be created, where any individual in the population is considered as a chromosome, 4 and this algorithm is inspired from evolution's theory by Darwin "survival of fittest."…”

Section: Introductionmentioning

confidence: 99%

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Alsghaier

Akour

2020

Softw Pract Exp

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Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

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“…Software Defect Forecast (SDF) is said to be a prediction and classification process which recognizes a software module has defect or not by monitoring its characteristics. The major aim of SFP is to identify the fault prone software modules by using some underlying software metrics before the beginning of testing phase [4]. Since, humans are dependent on software in day to day life a fault in a software module may cause severe effects in human lives [5].…”

Section: Introductionmentioning

confidence: 99%

Software Fault Prediction and Classification using Cost based Random Forest in Spiral Life Cycle Model

Premalatha¹,

Srikrishna²

2018

IJIES

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Abstract:In the domain of software engineering many new techniques are deployed for identifying the fault in software modules. This part of software design plays a fundamental role cause of its assurance towards higher reliability and stability. Many existing techniques like Bayesian approach have been employed to minimize the software faults but they can't able to predict efficiently within limited resources. In this paper, a new classification and prediction methodology is put forth to progress the accuracy of defect forecast based on Cost Random Forest algorithm (CRF) which reduces the effects of faults in irrelevant software modules. The proposed algorithm predicts the quantity of faults present in the modules of software in less time and classify based on measures of similarity obtained from Robust Similarity clustering technique. The overall results inferred from this methodology proven that this CRF can be capable to rank the module's faults in order to enhance the software development quality.

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Towards an ensemble based system for predicting the number of software faults

Cited by 87 publications

References 64 publications

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software Fault Prediction and Classification using Cost based Random Forest in Spiral Life Cycle Model

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