SDPTool : A tool for creating datasets and software defect predictions

Pachouly, Jalaj; Ahirrao, Swati; Kotecha, Ketan

doi:10.1016/j.softx.2022.101036

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Jalaj Pachouly, Swati Ahirrao et.al. [7] have done a Systematic Literature Review which focuses on datasets and methods for data validation, it also focus on Defect detection and prediction approaches, tools used, it also gives recommendations for future researchers in the area of Artificial Intelligence. In this review they have tried to list all approach on ML and DL etc.…”

Section: Machine Learning Based Sdpmentioning

confidence: 99%

Machine Learning Based Approach for Software Defect Prediction using Hyperparameter

Sharma,

Yadav

2024

Preprint

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Software Defect Prediction (SDP) is an assessment done for software or IT (Information Technology) solution. it enables stakeholders to judge its quality, functionality, scalability, reliability, information security and availability during SDLC of software. With digitalization of business and processes, its scope has multiplied since business look for reliable and good quality solutions for important application. Since most of these activities are done manually, it has been an area of research in software engineering. Researchers have been trying to predict defects from code metrics taken from PROMISE software repository dataset like CM1, JM1 and KC1. In this paper we have developed Model based on hyperparameter tuning for well accepted Machine Learning classifier to predict Software defects and found it better compared to many earlier proposed Model.

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Section: Machine Learning Based Sdpmentioning

confidence: 99%

Machine Learning Based Approach for Software Defect Prediction using Hyperparameter

Sharma,

Yadav

2024

Preprint

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“…A robust tool was introduced in [4] that initially with the aid of raw data collection following with validation finally predicted the defect by means of machine learning. Multiple expert learning systems were integrated in [5] to decide on the faulty modules with elevated accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

T Shathish Kumar

2024

jes

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Predicting defective software modules before testing is a valuable operation that reduces time and cost of software testing. Source code fault prediction plays a vital role in improving software quality that effectively assists in optimization testing resource allocation. Several machine learning and ensemble learning techniques has been extensively evolved over the recent few years to predict defect at an early stage. These techniques made predictions based on historical defect data, the software metrics. Nevertheless, the time efficient and accurate fault predictions are the major challenging tasks that yet have to be addressed. In order to ensure accurate software fault prediction, a method called Rand-Index Target Projective Gradient Deep Belief Network (RTPGDBN) is designed. The proposed RTPGDBN method comprises of three processes namely acquiring JAVA packages, software metric selection and classification. First, the number of JAVA packages is used as input from the dataset. Second with the JAVA packages obtained as input, Rand similarity indexive target projection function is applied for selecting the most significant software metrics in order to minimize time complexity of fault prediction. Third, with the selected metrics, classification is performed using Tversky Gradient Deep Belief neural network. Also, gradient descent function is applied to minimize classification error, therefore ensuring accurate software fault classification results obtained at the output layer. Experimental setup of proposed RPGDDBN and existing methods are implemented in Java language and the dataset collected from smell prediction replication package. Performance analysis is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity and space complexity. Through extensive experiments on repository data, experimental results indicate that our RPGDDBN method outperforms two state-of-the-art defect detection methods in terms of different performance metrics.

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“…Bibliometrics is another approach to synthesise knowledge [15]. Similarly to the above, the only two bibliometrics studies concerned with software quality were focused on specific aspects of software quality, one on defect prediction [16] and the other on code smells [17].…”

Section: Introductionmentioning

confidence: 99%

Software Quality: How Much Does It Matter?

Kokol

2022

Electronics

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Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.

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SDPTool : A tool for creating datasets and software defect predictions

Cited by 4 publications

References 2 publications

Machine Learning Based Approach for Software Defect Prediction using Hyperparameter

Machine Learning Based Approach for Software Defect Prediction using Hyperparameter

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

Software Quality: How Much Does It Matter?

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