Cross-Project Software Fault Prediction Using Data Leveraging Technique to Improve Software Quality

Khan, Bilal; Iqbal, Danish; Badshah, Sher

doi:10.1145/3383219.3383281

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Madera and Tomon apply machine learning to identify source code artefacts that are probably endangered by software defects [96]. Software defect prediction is highly influenced by the amount of availability of data for training the machine learning models, including neural networks, SVM, KNN, K-Means Clustering, Naive Bayes, decision trees, logistic and linear regression models, as well as their combinations with ensemble learning [97]. Khan et al use transfer learning to utilise the data of different projects and to overcome the data availability barrier [97].…”

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

“…Software defect prediction is highly influenced by the amount of availability of data for training the machine learning models, including neural networks, SVM, KNN, K-Means Clustering, Naive Bayes, decision trees, logistic and linear regression models, as well as their combinations with ensemble learning [97]. Khan et al use transfer learning to utilise the data of different projects and to overcome the data availability barrier [97]. Blas predicts the quality of the software by means of simulation and modelling based on the architecture defined in [98].…”

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

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Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

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Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

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Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

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“…The classification model for predicting cost slippage using data mining techniques uses the budget and schedule for the initial planning of an ICT project, and then forecasts a cost slip in the project category. There are three categories of fall that are deemed natural, of medium slip, and high fall, which require action [115,164]. The goal is to explain how a classification model is built using data mining techniques to forecast cost losses.…”

Section: Recommendations For Software Fault Prediction Modelsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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