Software Fault Prediction using Computational Intelligence Techniques: A Survey

Ranjan, Prabhat; Kumar, Sushant; Kumar, Uttam

doi:10.17485/ijst/2017/v10i18/112324

Cited by 13 publications

(13 citation statements)

References 23 publications

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“…Precision: The fraction of the total number of defective classes among the overall categorized defective class. 42,48,58,78,[90][91][92] T A B L E 1 3 Validation analysis on the basis of quality of studies in the SDP context.…”

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

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A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

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Software defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting.

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Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

“…Recall is a ratio of properly identified positive instances to the total number of positive instances, whereas precision is a measure of correctly categorized positive instances out of all positive cases. 42,48,53,54,78,87,[90][91][92]…”

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

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“…The outcomes of the survey exhibited that the proposed method's performance differs with varied datasets. In addition, the article 7 aimed to determine the methodologies which have been utilized in SFP. This study also included various computational intelligence methodologies namely data mining, soft computing and ML based SFP.…”

Section: Review Of Existing Workmentioning

confidence: 99%

MLPNN‐RF: Software fault prediction based on robust weight based optimization and Jacobian adaptive neural network

Babu¹,

Sivasubramanian

Natarajan

2022

Concurrency and Computation

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Software fault prediction (SFP) is a vital objective in software engineering. This might permit effective resource allocation and also enhance informed decisions about the release quality. SFP is being a critical issue for software professionals as well as the tech industry. Thus, SFP is necessary. The study intends to perform efficient error rate estimation using the proposed hybrid robust weight based optimization and Jacobian adaptive neural network (RWO-JANN). It also aims to classify the software faults in an efficient way using multi-layer perceptron neural network-random forest (MLPNN-RF). Various processes are involved to accomplish SFP. At first, the dataset is taken as input. After this, data preprocessing is performed. Subsequently, weights are initialized using the proposed RWO-JANN. Weight initialization is performed through a series of steps. Then, the position and the weight parameter are updated to perform weight initialization. After this, the error rate is estimated and the weight is updated on the basis of the learning rate and Jacobian matrix calculation. Lastly, the decay rate is analyzed. If the error rate extends beyond the threshold value, the process repeats from weight initialization. If not, the testing process is performed and lastly the classified output for SFP is obtained by the proposed MLPNN-RF. The proposed system is comparatively analyzed with the existing methods in terms of accuracy, precision, recall, F1 score, sensitivity, specificity, and error rate. The analytical results revealed effective outcomes of proposed system than the existing techniques with accuracy of 99.01%.

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“…As stated by Kaur and Sharma [2] all software have the tendency to contain faults (situations influencing a software to carry out its desired operation efficiently) as it is almost impossible to produce fault free software [3]. It is important to also note that not all of these faults result in failure (inability of a software system to perform as specified) [4]. Therefore, one of the objectives of fault prediction in software is to reduce bugs (faults) before failure occurs.…”

Section: Introductionmentioning

confidence: 99%

“…; Parvinder et al [19] used genetic algorithm for early detection of defect in software modules; Kanmani et al [20] applied neural network in object-oriented software to predict defect; Malhotra and Jain [9] estimated fault proneness using object-oriented Chidamber and Kemerer (CK) metrics and Quality Model for Object Oriented Design (QMOOD) metrics by applying logistic regression and six machine learning methods (Random Forest, Adaboost, bagging, Multi-layer perceptrons, Support Vector Machine (SVM) and Genetic Programming). Nair et al [10] used decision tree in predicting software defect and compared the performance with three other intelligence techniques namely Logistic Regression, Multi-layer Perceptrons and SVM; Reshi and Singh [21] used smell prediction model based on SVM to predict defects in releases of eclipse software; Ranjan et al [4] explored the different techniques used in software fault prediction such as soft computing, data mining and machine learning approaches as well as their accuracy rates using Random Forest, Decision Tree Regression, Neural Network, Genetic Algorithm, SVM, Artificial Neural Network and Fuzzy Logic; Fan et al [6] proposed a framework called Defect Prediction via Attention-based Recurrent Neural Network (DP-ARNN) which automatically learned syntactic and semantic features for accurate defect prediction. Seven Java projects in Apache were chosen to validate the framework using F1 measure and Area Under Curve (AUC) as evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

Eyoh¹,

Udo²,

Umoeka³

2021

IJASRE

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With the continuous expansion and innovations in modern software development, the rate at which defects are present in software is directly proportional to how sophisticated and complex the software tends to be. Software fault prediction therefore continues to remain an important area of research in software engineering especially as new modeling algorithms are still emerging. In spite of the fact that the potential implementations of fuzzy set theory in software fault prediction have been explored in the past, to the best knowledge of the authors, it has not yet examined how interval type-2 intuitionistic fuzzy sets with membership and non-membership degrees could be used with parameter optimization in this domain. Therefore, this work aims to adopt an interval type-2 intuitionistic fuzzy logic system to predict fault in the requirement phase of Software Development Life Cycle. Intuitionistic fuzzy logic system deals with uncertainty using separate degrees of membership and non-membership of an element to a set as well as hesitation index, therefore becoming more appropriate and flexible tool to deal with imprecision and vagueness in data. Experimental analyses show that the obtained software prediction outputs using interval type-2 intuitionistic fuzzy logic system are very close to the actual outputs which confirm that the proposed approach is a more realistic alternative for software fault prediction.

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Software Fault Prediction using Computational Intelligence Techniques: A Survey

Cited by 13 publications

References 23 publications

A research landscape on software defect prediction

A research landscape on software defect prediction

MLPNN‐RF: Software fault prediction based on robust weight based optimization and Jacobian adaptive neural network

Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

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