Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Khan, Muhammad Sufyan; Jabeen, Farhana; Ghouzali, Sanaa; Rehman, Zobia; Naz, Sheneela; Abdul, Wadood

doi:10.1109/access.2021.3072380

Cited by 44 publications

(28 citation statements)

References 61 publications

(141 reference statements)

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“…DNNs resulted in relatively higher prediction accuracy in a study by Mensah et al 17 against two benchmarks, namely, the ATLM and ordinary least squares regression 73 . DNN models have similarly yielded improved prediction accuracy in previous studies 62,74–79 . Specifically, we setup a DNN, which makes use of multiple hidden layers with their respective neurons and an output layer with two neurons (for the duplex variables ) to automatically learn from a set of projects and provide the resulting predictions for the targets.…”

Section: Methodsmentioning

confidence: 99%

“…73 DNN models have similarly yielded improved prediction accuracy in previous studies. 62,[74][75][76][77][78][79] Specifically, we setup a DNN, which makes use of multiple hidden layers with their respective neurons and an output layer with two neurons (for the duplex variables) to automatically learn from a set of projects and provide the resulting predictions for the targets. The DNN used in this study has two hidden layers with 5 and 2 neurons, respectively.…”

Section: Methodsmentioning

confidence: 99%

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A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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“…Metaheuristic algorithms are often nature-inspired computational intelligence methods for optimal solution approximation (Khan et al 2021). Recently, various metaheuristics, such as ABC, GA, GWO, PSO, and WCA, have been widely utilized for landslide displacement prediction due to their optimization strengths.…”

Section: Metaheuristic Algorithms For Hyperparameter Optimization Of Svrmentioning

confidence: 99%

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Xia

Guo

et al. 2022

Landslides

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Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

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“…While some studies focus on improving estimationlearning processes through formal models, such as machine learning [2,[13][14][15], expert-based methods are the dominant approach used in agile software development [9]. The agile community believes that the agile development process facilitates developers' learning from short and frequent feedback loops [16].…”

Section: Introductionmentioning

confidence: 99%

Estimating Efforts for Various Activities in Agile Software Development: An Empirical Study

Cao

2022

IEEE Access

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Effort estimation is an important practice in agile software development. The agile community believes that developers' estimates get more accurate over time due to the cumulative effect of learning from short and frequent feedback. However, there is no empirical evidence of an improvement in estimation accuracy over time, nor have prior studies examined effort estimation in different development activities, which are associated with substantial costs. This study fills the knowledge gap in the field of software estimation in agile software development by investigating estimations across time and different development activities based on data collected from a large agile project. This study investigated effort estimation in various development activities, including feature development, bug fixing, and refactoring in agile software development. The results indicate that estimation of agile development does not improve over time, as claimed in the literature. Our data also indicate that no difference exists in the magnitude of estimation errors between feature tasks and bug-fixing/refactoring tasks, while bug-fixing and refactoring tasks are overestimated more frequently than feature tasks. This study also contributes to our knowledge about overestimation and underestimation patterns in agile software development.INDEX TERMS agile software effort estimation, bug-fixing effort estimation, refactoring effort estimation

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Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Cited by 44 publications

References 61 publications

A classification scheme to improve conclusion instability using Bellwether moving windows

A classification scheme to improve conclusion instability using Bellwether moving windows

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Estimating Efforts for Various Activities in Agile Software Development: An Empirical Study

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