Filtering of Inconsistent Software Project Data for Analogy-Based Effort Estimation

Le-Do, Tuan Khanh; Yoon, Kyung-A; Seo, Yeong-Seok; Bae, Doo‐Hwan

doi:10.1109/compsac.2010.56

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…These methods are Mantel's correlation based deletion [9] and EID based deletion [15]. We applied these methods to analogy-based estimation, to confirm their effects.…”

Section: Outlier Deletion Methodsmentioning

confidence: 99%

“…Using EID (Effort Inconsistency Degree), EID based deletion identifies an outlier when the case's effort is inconsistent with similar cases' one [15]. The method is designed to apply to analogy-based estimation.…”

Section: Eid Based Deletionmentioning

confidence: 99%

“…Five types of outlier deletion methods (EID (Effort Inconsistency Degree) based deletion [15], Mantel's correlation based deletion [9], Cook's distance based deletion, k-means based deletion [25], and LTS (Least trimmed squares) based deletion [25]) were applied to the ISBSG [8], Kitchenham [14] and Desharnais datasets [7], with development effort estimated by analogy-based estimation. These datasets include data about many projects collected from software development companies, and are widely used in many researches.…”

Section: Introductionmentioning

confidence: 99%

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An empirical evaluation of outlier deletion methods for analogy-based cost estimation

Tsunoda

Kakimoto

Monden

et al. 2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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Background: Any software project dataset sometimes includes outliers which affect the accuracy of effort estimation. Outlier deletion methods are often used to eliminate them. However, there are few case studies which apply outlier deletion methods to analogy-based estimation, so it is not clear which method is more suitable for analogy-based estimation. Aim: Clarifying the effects of existing outlier deletion methods (Cook's distance based deletion, LTS based deletion, k-means based deletion, Mantel's correlation based deletion, and EID based deletion) and our method for analogy-based estimation. Method: In the experiment, outlier deletion methods were applied to three kinds of datasets (the ISBSG, Kitchenham, and Desharnais datasets), and their estimation accuracy evaluated based on BRE (Balanced Relative Error). Our method eliminates outliers from the neighborhoods of a target project when the effort is extremely different from other neighborhoods. Results: Deletion methods which are designed to apply to analogy-based estimation (i.e. Mantel's correlation based deletion, EID based deletion, and our method) showed stable performance. Especially, only our method showed over 10% improvement of the average BRE on two datasets. Conclusions: It is reasonable to apply deletion methods designed for analogy-based estimation, and more preferable to apply our method to analogybased estimation.

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“…These methods are Mantel's correlation based deletion [9] and EID based deletion [15]. We applied these methods to analogy-based estimation, to confirm their effects.…”

Section: Outlier Deletion Methodsmentioning

confidence: 99%

Section: Eid Based Deletionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

Tsunoda

Kakimoto

Monden

et al. 2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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“…Once in a time, software development was very small and small in nature, but the cost of the software project was vast. However, things have changed like anything now the common man can reach to solve their problems [4,5]. In this context, and huge demand in industry and the healthy competition software industry has to quote an exact figure on the product.…”

Section: Introductionmentioning

confidence: 99%

A CASE Study on Software Project Development Cost, Schedule & Effort Estimation

Rao

Ramaiah

2017

Asian J Pharm Clin Res

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This paper theme is to provide a case study of Software Project Development cost, effort, and schedule estimation. From recent past, a remarkable research takes place in developing different techniques on software effort and cost estimation. Making estimation before start of any project is necessary to be able to plan and manage any project. The estimate is an intelligent guess for the project resources. Nowadays, software has become a major contributor to economic growth for any nation. Making an estimate before starting any software project is vital for the project managers and key stakeholders. Major project milestones such as project schedules, budgeting, resource allocation, and project delivery dates are set on the effort and cost estimates. Thus, the reliability of the estimation leads any project success or otherwise fail. In this article, author's idea is to work with function point analysis and include the concept of workforce scheduling in a better way while taking the decision in the contract phase. That leads to strengthening the relations between the developer and the customer. Basically, size is a main measured unit of the software project. Based on the size and other functionalities, the software managers estimate the total effort required to develop the project. From the effort and work schedule, the total cost can be estimated.

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“…Huang et al, (2007) developed a fuzzy neural network by applying artificial neural networks to fuzzy inference processes. Le-Do et al, (2010), have proposed a scheme for filtering the Inconsistent Software Project Data for Analogy-based effort Estimation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features

M.¹

2012

Journal of Computer Science

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Problem statement:The spiraling growth of IT industry has witnessed an unprecedented change in the software development paradigm, from algorithmic models to machine learning techniques. At present, there are no standard methods to predict the accuracy of software cost estimation, which is an important goal of the software community. Approach: This study proposes a simple and systematic algorithmic procedure for analogy based software cost prediction to detect the aberrant data points. The algorithm is analyzed and correlated with the Desharnais and NASA datasets containing all adaptive features with numerical and categorical variables. Results: The interpreted curves using the above datasets depict a discernible anomaly for the dataset having more categorical variables, thereby indicating the erroneous data points. Conclusion: The elimination of aberrant data points using the new algorithmic method improves the accuracy of software cost estimation using historical data sets.

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Filtering of Inconsistent Software Project Data for Analogy-Based Effort Estimation

Cited by 14 publications

References 11 publications

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

A CASE Study on Software Project Development Cost, Schedule & Effort Estimation

Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features

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