Particle Swarm Optimization for Predicting the Development Effort of Software Projects

Mariana Dayanara Alanis-Tamez,Yenny Villuendas-Rey,Cuauhtémoc López-Martín

doi:10.3390/math8101819

Mariana Dayanara Alanis-Tamez, Yenny Villuendas-Rey + Show 1 more

Open Access

https://doi.org/10.3390/math8101819

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Abstract

Software project planning includes as one of its main activities software development effort prediction (SDEP). Effort (measured in person-hours) is useful to budget and bidding the projects. It corresponds to one of the variables most predicted, actually, hundreds of studies on SDEP have been published. Therefore, we propose the application of the Particle Swarm Optimization (PSO) metaheuristic for optimizing the parameters of statistical regression equations (SRE) applied to SDEP. Our proposal incorporates two elements in PSO: the selection of the SDEP model, and the automatic adjustment of its parameters. The prediction accuracy of the SRE optimized through PSO (PSO-SRE) was compared to that of a SRE model. These models were trained and tested using eight data sets of new and enhancement software projects obtained from an international public repository of projects. Results based on statistically significance showed that the PSO-SRE was better than the SRE in six data sets at 99% of confidence, in one data set at 95%, and statistically equal than SRE in the remaining data set. We can conclude that the PSO can be used for optimizing SDEP equations taking into account the type of development, development platform, and programming language type of the projects.

Highlights

Software engineering management involves planning [1]
The applied algorithms have been artificial bee colony (ABC) [25], cuckoo search [26], differential evolution [27], GA [28], Particle Swarm Optimization (PSO) [29], simulated annealing [30], tabu search [30], and whale optimization algorithm [31]. In those ten studies that we identified where PSO was applied to Software development effort prediction (SDEP), PSO has been used to optimize parameters of models such as Bayesian belief network [32], CBR [33,34,35,36,37], COCOMO statistical equation [38], decision trees [40], fuzzy logic [29], mathematical expressions [25], neural networks [40], and support vector regression [40]
After applying the PSO-statistical regression equations (SRE), it is possible to detail the selected SDEP model by data set

Summary

Introduction

Software engineering management involves planning [1]. The software project planning includes software prediction, and the most common predicted variables have been size [2] (mainly measured in either source lines of code, or function points [3]), effort (in person-hours or person-months [3]), duration (in months [4]), and quality (in defects [5]).Software development effort prediction (SDEP), termed effort estimation or cost estimation [6], is needed for managers to estimate the monetary cost of projects. In USA the cost by person-month (which is equivalent to 152 person-hours) is of $8000 USD [7] Those projects taking more time (i.e., time overrun) costing more money (i.e., cost overrun) [8], and cost overrun has been identified as a chronic problem in most software projects [9]; whereas for cost underrun, a portion of the budgeted money is not spent and money taxes have to be paid. These issues related to costs have been the causes for which a software project has been assessed based upon the ability to achieve the budgeted cost [10,11]

Results

Discussion

Conclusion