Software Cost Estimation Using Artificial Neural Network

Arora, Shaina; Mishra, Nidhi

doi:10.1007/978-981-10-5699-4_6

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…Boehm et al 20 were the first to propose a well‐known model called the COCOMO for predicting software cost‐effectively and a total of 63 projects is used to validate the model's effectiveness. Over the last three decades, lots of new SCE models based on algorithmic, soft computing, machine learning, and meta‐heuristic techniques have been proposed 6,21‐25 . Benala et al 26 introduced an approach based on DE with five mutation strategies.…”

Section: Background Methods and Software Cost Estimationmentioning

confidence: 99%

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Gouda

Mehta

2022

Softw Pract Exp

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One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta‐heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism‐based self‐adaptive mutation operator to solve the multi‐objective optimization problems. This approach addresses the issues that exist in multi‐objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism‐based self‐adaptive mutation operator in a multi‐objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non‐dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi‐objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi‐objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

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Section: Background Methods and Software Cost Estimationmentioning

confidence: 99%

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Gouda

Mehta

2022

Softw Pract Exp

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“…Pospieszny et al [31], Mensah et al [26], Puspaningrum and Sarno [32], Moosavi and Bardsiri [27], Wani and Quadri [40], Arora and Mishra [3], Abnane et al [1], and Khuat and Le [20] respectively employed a linear method integrated with neural networks, multivariate regression model, artificial neural networks integrated with the harmony algorithm, neural networks integrated with the fuzzy method, neural networks, the fuzzy model and the artificial bee colony algorithm for software development cost estimation.…”

Section: Introductionmentioning

confidence: 99%

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Moradbeiky

Bardsiri

Jafari

2020

cai

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Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank.

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Neural Networks‐Based Software Development Effort Estimation: A Systematic Literature Review

Boujida,

Amazal,

Idri

2024

J Software Evolu Process

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Software development effort estimation (SDEE) is a key task in managing software projects. Among the existing SDEE models, artificial neural networks (ANN) have garnered considerable attention from the software engineering community because of their ability to learn from previous data and yield acceptable estimates. However, to the best of the authors' knowledge, no systematic literature review (SLR) has been carried out with focus on the use of ANNs in SDEE. This work aims to analyze ANN‐based SDEE studies from five view‐points: estimation accuracy, accuracy comparison, estimation context, impact of combining ANN‐based SDEE models with other techniques, and ANNs parameters. To find relevant ANN‐based SDEE studies, we carried out an automated search using four electronic databases. The quality of the relevant papers was assessed to determine the set of papers to include in our review. We identified 65 papers published in the period 1993–2023 with acceptable quality score. The results of our systematic review revealed that ANN‐based SDEE models perform better than 11 machine learning (ML) and non‐ML SDEE models. Further, the estimation accuracy is improved when neural networks are used in combination with other techniques such as fuzzy clustering techniques. This study found that the use of ANN models in SDEE is promising to get accurate estimates. However, the application of ANN models in industry is still limited. Therefore, it is recommended that practitioners cooperate with researchers to encourage and facilitate the application of ANN models in industry.

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Software Cost Estimation Using Artificial Neural Network

Cited by 8 publications

References 8 publications

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Neural Networks‐Based Software Development Effort Estimation: A Systematic Literature Review

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