A systematic review of studies on use case points and expert‐based estimation of software development effort

Mahmood, Yasir; Kama, Nazri; Azmi, Azri

doi:10.1002/smr.2245

Cited by 31 publications

(27 citation statements)

References 56 publications

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“…Systematic reviews describe several types of prediction techniques such as expert judgment, 6 statistical and ML techniques, 7,11 only on ML techniques, 3,20 ensemble effort techniques, 35 and on specific techniques such as analogy based, 34 and genetic programming (GP). 36 These techniques can be classified into the following two general categories:…”

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

“…It is based on an intuition-based quantification and has been reported as the most commonly used technique due to its simplicity and flexibility; however, it has not been well defined if expert judgment is better than algorithmic models because of expert judgment has not been widely researched as some of the formal techniques. 6 Prediction based on expert judgment is useful when data are scarce such that models can be built.…”

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

“…19 The published systematic reviews on hundreds of studies about SDLC effort prediction have been built having as exclusion criterion those studies aimed at specific phases such as testing or maintenance. 3,6,7,20 A few studies have proposed models to predict the effort of phases such as testing 21 or maintenance 22 ; however, I did not find any study whose objective has been to propose a model to predict specifically the construction effort (CE). Figure 1 depicts the SDLC phases and those activities specifically related to the construction phase.…”

mentioning

confidence: 99%

“…I did not identify any study on software effort prediction models whose objective has been the application of a SVR-SMO-PUK. 3,6,7,11,12,20,[34][35][36][37] 3. I identified nine studies that apply SVR to effort prediction.…”

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confidence: 99%

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Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

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Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

Section: Studies On Software Effort Prediction Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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“…Goldratt suggests that the estimations should be created by an experienced person, preferably a colleague who will execute the task. Expert-based estimation of software development has been investigated quite extensively and is often considered on equal footing with formal, model-based estimations (Mahmood, et al, 2020). To build on the earlier metaphor: in case you know how to travel to the nearest city, then you can more accurately estimate the time needed to do so.…”

Section: Theory and Hypothesesmentioning

confidence: 99%

Time Estimation Improvement in Agile Development

2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

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Estimating the time needed to execute an IT project has turned out to be a major challenge in daily praxis. Despite high inaccuracies, these estimations are important as they can be used to determine the cost for offering an IT project and in deciding whether to execute it at all. In this study we focused on the estimations for user stories created by a European Software company delivering 100% of their software in an agile way of working. Over a time-span of 10 months, 1738 estimations were created on small user stories (around 5 hours of work on average). Three main conclusions could be drawn from the data. Firstly, it was found that smaller-than-two-hour user stories were over-estimated roughly as frequently as under-estimated. In contrast, estimations for larger-than-two-hour user stories were less balanced and on average too low (under-estimated). Secondly, and surprisingly, we did not see that estimations were better when the estimator was able to execute the task himself versus when he was not. The statistics suggest that estimators who are able to do the task themselves tend to underestimate the time needed to deliver the user story. Finally, we found that the accuracy of estimations does not improve over time.

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Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Nhung,

Van Hai,

Silhavy

et al. 2023

J Software Evolu Process

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Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p‐value for the t‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

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A systematic review of studies on use case points and expert‐based estimation of software development effort

Cited by 31 publications

References 56 publications

Effort prediction for the software project construction phase

Effort prediction for the software project construction phase

Time Estimation Improvement in Agile Development

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

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