An empirical approach for early estimation of software testing effort using SRS document

Sharma, Ashish; Kushwaha, Dharmender Singh

doi:10.1007/s40012-012-0003-z

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…This comprises techniques such as regression [32], [33], [34], ANN [13], [35], [36], [37], Ensemble [31], [38], [39], and Blockchain [40]. The specific focus of research in all these methods has been on the optimization of errors [41], [42], [43], [44], [45].…”

Section: Computational Intelligencementioning

confidence: 99%

Software Cost and Effort Estimation: Current Approaches and Future Trends

Rashid,

Shafi,

Ahmad

et al. 2023

IEEE Access

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Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations.

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Section: Computational Intelligencementioning

confidence: 99%

Software Cost and Effort Estimation: Current Approaches and Future Trends

Rashid,

Shafi,

Ahmad

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The contemplation is that, if the entire software is implemented based on this document, can we not estimate the various software development activities too from this SRS? Since complexity analysis has an extremely high payoff for the investment, hence it is necessary to carry out a precise and accurate estimation of software complexity for software to be developed [1].…”

Section: Estimation Of Software Complexity Using Srsmentioning

confidence: 99%

Effective Estimation Software cost using Test Generations

Feruza¹,

Alisherov²

2011

AJMAHS

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In software engineering, we have many problems particularly with software cost estimation and the assessment of the evaluation model. Software cost estimation mainly depends on two factors which are the estimation consistency and estimation accuracy. These are very much important to the perfect development of the software without any drawbacks to it. In the past there have been many models and techniques that have failed to give the correct software estimation that really challenges the all factors of perfect software in this paper we use software requirement specification but we were not getting the accurate results that are helpful for developing of perfect software that is having perfect cost estimation. So we introduce the Automated Test-Data Generation Techniques method that gives us the desired results which will be helping us to know the software cost estimation very much accurately and also estimate consistently.

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“…Software quality is directly associated with software testing (ST) [1], which influences the cost, and success of a software project [2] [3] [4] [5]. To avoid losing customers, software companies are investing considerable resources in ST to release defect-free products [6] [7].…”

Section: Introductionmentioning

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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An empirical approach for early estimation of software testing effort using SRS document

Cited by 10 publications

References 45 publications

Software Cost and Effort Estimation: Current Approaches and Future Trends

Software Cost and Effort Estimation: Current Approaches and Future Trends

Effective Estimation Software cost using Test Generations

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

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