Software Development Effort Estimation Using Fuzzy Logic: A Case Study

Martín, Cuauhtémoc López; Pasquier, Jérôme Leboeuf; Yáñez, Cornelio; Tornes, Agustín Gutiérrez

doi:10.1109/enc.2005.47

Cited by 41 publications

(26 citation statements)

References 9 publications

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“…Least Median Square (LMS) technique is one of the factual strategies for unravelling the conditions which are more than obscure [5]. This strategy relatively utilized as a part of systematic relapse.…”

Section: Least Median Squarementioning

confidence: 99%

“…The key concept structure of COCOMO II to future programming commercial center divisions depicted above; Key of data sources and yields of the COCOMOII sub models/methods to the level of data accessible. This enable sub models to be customized to a venture's specific procedure technique [5]. COCOMO II takes after the transparency standards utilized as a part of the first COCOMO.…”

Section: Cocomomentioning

confidence: 99%

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Probabilistic estimation of software development effort techniques using machine learning

Sagar¹,

Moparthi²,

Mandhala³

2018

IJET

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Precisely assessing programming exertion is likely the greatest test confronting for programming engineers. Assessments done at the prop-osition arrange has high level of incorrectness, where prerequisites for the degree are not characterized to the most reduced subtle elements, but rather as the venture advances and necessities are explained, exactness and certainty on appraise increments. It is vital to pick the correct programming exertion estimation systems for the forecast of programming exertion. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been utilized on guarantee dataset for forecast of programming exertion in this article.

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Section: Least Median Squarementioning

confidence: 99%

Section: Cocomomentioning

confidence: 99%

Probabilistic estimation of software development effort techniques using machine learning

Sagar¹,

Moparthi²,

Mandhala³

2018

IJET

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“…However, the accuracy of an estimation technique is proportional to Pred (25), Pred (25) and inversely proportional to the MMRE (Marza and Seyyedi, 2009;Martin et al, 2005). According to (Stensrud, 2003), MMRE is used for two kinds of assessments (at least).…”

Section: Evaluation Techniques Criteriamentioning

confidence: 99%

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

Elzamly

Hussin

2014

Journal of Computer Science

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Risk is not always avoidable, but it is controllable. The aim of this study is to identify whether those techniques are effective in reducing software failure. This motivates the authors to continue the effort to enrich the managing software project risks with consider mining and quantitative approach with large data set. In this study, two new techniques are introduced namely stepwise multiple regression analysis and fuzzy multiple regression to manage the software risks. Two evaluation procedures such as MMRE and Pred (25) is used to compare the accuracy of techniques. The model's accuracy slightly improves in stepwise multiple regression rather than fuzzy multiple regression. This study will guide software managers to apply software risk management practices with real world software development organizations and verify the effectiveness of the new techniques and approaches on a software project. The study has been conducted on a group of software project using survey questionnaire. It is hope that this will enable software managers improve their decision to increase the probability of software project success.

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“…It is considered to be one of most cited, best known and most plausible effort and cost prediction model [11]. Though it was one of the stable models of its time but it had number of drawbacks.…”

Section: Cocomo Framework and Fuzzylogicmentioning

confidence: 99%

Software Development Effort Estimation Using Soft Computing

Kad¹,

Chopra²

2011

IJMLC

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Abstract-Software development effort estimation is a daunting task that is being carried out by software developers as not much of the information about the software which is to be developed is available during the early stages of development. The information that is to be gathered for various attributes of software needs to be subjective which otherwise leads to imprecision and uncertainty. Inaccurate estimation of the software effort and schedule leads to financial loses and also delays in project deadline. In this paper, we present the use of soft computing technique to build a suitable model which improves the process of effort estimation.

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Software Development Effort Estimation Using Fuzzy Logic: A Case Study

Cited by 41 publications

References 9 publications

Probabilistic estimation of software development effort techniques using machine learning

Probabilistic estimation of software development effort techniques using machine learning

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

Software Development Effort Estimation Using Soft Computing

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