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Demonstrates improved prediction of software construction effort using advanced algorithms and neural networks, indicating valuable budgeting techniques for managers.
Software construction corresponds to one of the software development life cycle (SDLC) activities. It consists of coding, unit testing, integration testing, and debugging activities. The SDLC effort (i.e. the number of person-hours) is an important variable predicted for budgeting the software projects. A common practice in software managers is to predict the effort by SDLC activity. Accordingly, we propose the incorporation of the Hooke-Jeeves (HJ) local search algorithm in particle swarm optimization (PSO), and genetic algorithms (GA) for tuning the hyperparameters of three types of neural networks, whose accuracies were compared to that of a statistical regression model. All of the models were applied for predicting the construction effort of software projects selected from an international public repository of software projects. Results allow concluding that a fully connected neural networks optimized with HJ, GA, and PSO can be used by software managers for predicting the construction effort of new or maintained software projects.
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Sanchéz-García et al. (2026) studied this question.
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