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Relevant bibliographies by topics / SOFTWARE PREDICTION MODELS

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Journal articles

Academic literature on the topic 'SOFTWARE PREDICTION MODELS'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "SOFTWARE PREDICTION MODELS"

1

Balogun, A. O., A. O. Bajeh, H. A. Mojeed, and A. G. Akintola. "Software defect prediction: A multi-criteria decision-making approach." Nigerian Journal of Technological Research 15, no. 1 (2020): 35–42. http://dx.doi.org/10.4314/njtr.v15i1.7.

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Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combinatio

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2

Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build de

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3

Vandecruys, Olivier, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, and Raf Haesen. "Mining software repositories for comprehensible software fault prediction models." Journal of Systems and Software 81, no. 5 (2008): 823–39. http://dx.doi.org/10.1016/j.jss.2007.07.034.

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4

Zaim, Amirul, Johanna Ahmad, Noor Hidayah Zakaria, Goh Eg Su, and Hidra Amnur. "Software Defect Prediction Framework Using Hybrid Software Metric." JOIV : International Journal on Informatics Visualization 6, no. 4 (2022): 921. http://dx.doi.org/10.30630/joiv.6.4.1258.

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Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predic

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5

Kalouptsoglou, Ilias, Miltiadis Siavvas, Dionysios Kehagias, Alexandros Chatzigeorgiou, and Apostolos Ampatzoglou. "Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction." Entropy 24, no. 5 (2022): 651. http://dx.doi.org/10.3390/e24050651.

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Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilit

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6

Shatnawi, Raed. "Software fault prediction using machine learning techniques with metric thresholds." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (2021): 159–72. http://dx.doi.org/10.3233/kes-210061.

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BACKGROUND: Fault data is vital to predicting the fault-proneness in large systems. Predicting faulty classes helps in allocating the appropriate testing resources for future releases. However, current fault data face challenges such as unlabeled instances and data imbalance. These challenges degrade the performance of the prediction models. Data imbalance happens because the majority of classes are labeled as not faulty whereas the minority of classes are labeled as faulty. AIM: The research proposes to improve fault prediction using software metrics in combination with threshold values. Stat

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7

Eldho, K. J. "Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models." Indian Journal of Science and Technology 15, no. 6 (2022): 237–42. http://dx.doi.org/10.17485/ijst/v15i6.2193.

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8

Karunanithi, N., D. Whitley, and Y. K. Malaiya. "Prediction of software reliability using connectionist models." IEEE Transactions on Software Engineering 18, no. 7 (1992): 563–74. http://dx.doi.org/10.1109/32.148475.

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9

Fenton, N. E., and M. Neil. "A critique of software defect prediction models." IEEE Transactions on Software Engineering 25, no. 5 (1999): 675–89. http://dx.doi.org/10.1109/32.815326.

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10

Lawson, John S., Craig W. Wesselman, and Del T. Scott. "Simple Plots Improve Software Reliability Prediction Models." Quality Engineering 15, no. 3 (2003): 411–17. http://dx.doi.org/10.1081/qen-120018040.

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