Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering

Gallego, Juan; Rodríguez, Daniel; Sicilia, Miguel Ángel; Rubio, Miguel Garre; García-Crespo, Ángel

doi:10.1007/s11390-007-9043-5

Cited by 24 publications

(17 citation statements)

References 11 publications

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“…The predictive performance obtained using clustering was similar to that obtained without clustering. Gallego et al (2007) partitioned CC projects into different subsets based on certain input attributes of interest. Each of the partitions was then further clustered using Expectation-Maximisation.…”

Section: Learning and Heterogeneity In Seementioning

confidence: 99%

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Minku

2019

Empir Software Eng

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Software effort estimation is an online supervised learning problem, where new training projects may become available over time. In this scenario, the Cross-Company (CC) approach Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving their collection cost. However, Dycom requires CC projects to be split into subsets. Both the number and composition of such subsets can affect Dycom's predictive performance. Even though clustering methods could be used to automatically create CC subsets, there are no procedures for automatically tuning the number of clusters over time in online supervised scenarios. This paper proposes the first procedure for that. An investigation of Dycom using six clustering methods and three automated tuning procedures is performed, to check whether clustering with automated tuning can create well performing CC splits. A case study with the ISBSG Repository shows that the proposed tuning procedure in combination with a simple threshold-based clustering method is the most successful in enabling Dycom to drastically reduce (by a factor of 10) the number of required WC training projects, while maintaining (or even improving) predictive performance in comparison with a corresponding WC model. A detailed analysis is provided to understand the conditions under which this approach does or does not work well. Overall, the proposed online supervised tuning procedure was generally successful in enabling a very simple thresholdbased clustering approach to obtain the most competitive Dycom results. This demonstrates the value of automatically tuning hyperparameters over time in a supervised way.

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Section: Learning and Heterogeneity In Seementioning

confidence: 99%

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Minku

2019

Empir Software Eng

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“…The predictive performance obtained using clustering was similar to that obtained without clustering. Gallego et al [10] partitioned CC projects into different subsets based on certain input attributes of interest. Each of the partitions was then further clustered using EM.…”

Section: Related Workmentioning

confidence: 99%

Clustering Dycom

Minku

Hou

2017

Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering

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Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and ExpectationMaximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets.

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“…But software repositories contain data from heterogeneous projects. Traditional application of regression equations to derive a single mathematical model results in poor performance [8]. The paper by Gallogo [8] has used Data clustering to solve this problem.…”

Section: Related Workmentioning

confidence: 99%

Software Effort Prediction using Statistical and Machine Learning Methods

Malhotra¹,

Jain²

2011

IJACSA

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-Accurate software effort estimation is an important part of software process. Effort is measured in terms of person months and duration. Both overestimation and underestimation of software effort may lead to risky consequences. Also, software project managers have to make estimates of how much a software development is going to cost. The dominant cost for any software is the cost of calculating effort. Thus, effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. There are various effort estimation models, but it is difficult to determine which model gives more accurate estimation on which dataset. This paper empirically evaluates and compares the potential of Linear Regression, Artificial Neural Network, Decision Tree, Support Vector Machine and Bagging on software project dataset. The dataset is obtained from 499 projects. The results show that Mean Magnitude Relative error of decision tree method is only 17.06%. Thus, the performance of decision tree method is better than all the other compared methods.

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Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering

Cited by 24 publications

References 11 publications

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Clustering Dycom

Software Effort Prediction using Statistical and Machine Learning Methods

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