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Context 1
... development team will provide the COSMIC sizing of an enhancement as well as an estimate of the corresponding effort. Figure 6 depicts the output via the user interface. Enhancement Request Details. ...
Context 2
... Request Details. The web interface page of the development team includes the enhancement details. Figure 6 contains two buttons: the blue button downloads the enhancement description validated by the product owner, and the green button generates a formal explanation of a specific enhancement request. Figure 7 shows a full formal description of a selected ER. ...
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Deep Learning (DL) is a branch of Machine Learning where models are developed using neural networks made of several layers for prediction. DL models have been developed to predict effort estimation in software development. This paper presents a review of works which discuss the use of DL models for effort estimation for Scrum. The various textual i...
Citations
... According to Idri et al. [17], ensemble learning models perform better than their single-model counterparts. Sakhrawi et al. [21,22] concluded that the SEM is a promising method of enhancing the precision of single models that are used to estimate the effort required to enhance conventional and scrum software. In both the conventional and agile contexts, the use of ensemble learning models has increased [23]. ...
... In both the conventional and agile contexts, the use of ensemble learning models has increased [23]. Multiple studies have used the SEM to estimate the amount of effort required during the development and maintenance phases only [17,21,22]. Ensemble learning models are used in the testing phase to estimate software defects. ...
A type of software testing, regression testing is often costly and labour-intensive. As such, multiple corporations have intensified efforts to estimate the amount of effort required. However, frequent alterations in software projects impact the precision of software regression test effort estimation (SRTEE), which increases the difficulty of managing software projects. Therefore, machine learning (ML) has increasingly been used to develop more accurate SRTEEs. The estimation process of a software project comprises inputs, the model, and outputs. This present study examines the quality of estimation inputs and the model required to deliver accurate estimation outputs. An SRTEE that uses the stacking ensemble model (StackSRTEE) was developed to increase the precision of SRTEE. It consisted of the three most common ML methods, namely neural networks, support vector regression, and decision tree regression. The grid search (GS) technique was then used to tune the hyperparameters of the StackSRTEE before it was trained and tested using a dataset from the International Software Benchmarking Standards Group (ISBSG) repository. The size of the functional change; specifically, enhancement; was used as the primary independent variable to improve the inputs of the StackSRTEE model. With the appropriate features; such as the functional change size of an enhancement; (1) the proposed StackSRTEE model yielded higher accuracy than the three individual ML methods on their own, (2) using GS to tune and set the individual ML methods increased the precision of the SRTEE outputs, and (3) the StackSRTEE-based GS tuning yielded estimations that were more precise.
