Threats to validity in search‐based predictive modelling for software engineering

Malhotra, Ruchika; Khanna, Megha

doi:10.1049/iet-sen.2018.5143

Cited by 10 publications

(4 citation statements)

References 104 publications

(146 reference statements)

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“…According to [54], there are four categories of valid threats in search-based predictive modeling for Software Engineering: conclusion, internal, external, and construction. In Table 15 we show the threats to validity, indicating how we addressed each one if it applied to our study.…”

Section: Threats To Validitymentioning

confidence: 99%

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Sánchez-García,

González-Hernández,

Cortés-Verdín

et al. 2024

Mathematics

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Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time.

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Section: Threats To Validitymentioning

confidence: 99%

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Sánchez-García,

González-Hernández,

Cortés-Verdín

et al. 2024

Mathematics

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“…There are four categories of valid threats in search-based predictive modeling for Software Engineering: conclusion, internal, external and construct [120]. The descriptions of the 17 validity threats, as well as the way in which they were addressed in our work are presented in Table 14.…”

Section: Validity Threatsmentioning

confidence: 99%

Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

Sánchez-García,

López-Martín,

Abran

2023

IEEE Access

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Software testing (ST) is one of the most important software development life cycle (SDLC) phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the literature ST effort percentage ranges from 10% to 60%. In the literature most of the machine learning algorithms and metaheuristics for optimizing them have looked at predicting overall SDLC effort without focusing on any specific SDLC phase, including testing. Therefore, this study investigates the application of the STEP of Gradient Boosting (GB) machine learning regression algorithm optimized through Differential Evolution (DE). Its prediction accuracy is compared with those obtained when the GB is also optimized through Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). The performance of GB-DE, GB-PSO, and GB-GA was also compared to that of statistical regression (SR). Seven data sets of actual projects were selected from an international public repository for software projects. The results showed that GB-DE was statistically better than SR in all seven data sets at 95% confidence, whereas GB-PSO and GB-GA were better than SR in four and three data sets, respectively. Thus, we can conclude that GB-DE can be used for STEP of either new projects or enhancement projects developed in either the third or fourth programming language generation.

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“…There are four types of validation threats regarding predictive modeling for software engineering. Each of them is defined, 71 and then described in accordance with the present study:…”

Section: Threats To Validitymentioning

confidence: 99%

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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Threats to validity in search‐based predictive modelling for software engineering

Cited by 10 publications

References 104 publications

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

Effort prediction for the software project construction phase

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