首页|Study Data from Aditya Institute of Technology and Management Update Knowledge o f Machine Learning (Improved Software Effort Estimation Through Machine Learning : Challenges, Applications, and Feature Importance Analysis)
Study Data from Aditya Institute of Technology and Management Update Knowledge o f Machine Learning (Improved Software Effort Estimation Through Machine Learning : Challenges, Applications, and Feature Importance Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Andhra Pradesh, India, by NewsRx editors, research stated, “Effort estimations are a crucial aspect of software development. The tasks should be completed before the start of any sof tware project.” Funders for this research include Deanship of Scientific Research At Majmaah Uni versity. Our news reporters obtained a quote from the research from Aditya Institute of T echnology and Management: “Accurate estimations increase the chances of project success, and inaccurate information can lead to severe issues. This study system atically reviewed the literature on effort-estimating models from 2015-2024, ide ntifying 69 relevant studies from various publications to compile information on various software work estimation models. This review aims to analyze the models proposed in the literature and their classification, the metrics used for accur acy measurement, the leading model that has been chiefly applied for effort esti mation, and the benchmark datasets available. The study utilized 542 relevant ar ticles on software development, cost, effort, prediction, estimation, and modell ing techniques in the search strategy. After 194 selections, the authors chose 6 9 articles to understand ML applications in SEE comprehensively. The researchers used a scoring system to assess each study’s responses (from 0 to 5 points) to their research questions. This helped them identify credible studies with higher scores for a comprehensive review aligned with its objectives. The data extract ion process identified 91% (63) of 69 studies as either highly or somewhat relevant, demonstrating a successful search strategy for analysis. The literature review on SEE indicates a growing preference for ML-based models in 5 9% of selected studies. 17% of the studies chosen fa vor hybrid models to overcome software development challenges. We qualitatively analyzed all the literature on software effort estimation using expert judgment, formal estimation techniques, ML-based techniques, and hybrid techniques. We di scovered that researchers have frequently used ML-based models to estimate softw are effort and are currently in the lead.”
Aditya Institute of Technology and Manag ementAndhra PradeshIndiaAsiaCyborgsEmerging TechnologiesMachine Lear ningSoftware
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