Software productivity and effort prediction with ordinal regression

Sentas, Panagiotis; Angelis, Lefteris; Stamelos, Ioannis; Bleris, G. L.

doi:10.1016/j.infsof.2004.05.001

Cited by 106 publications

(93 citation statements)

References 17 publications

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“…The predictive process [1] Predicting software development effort with high precision is still a great challenge for project managers. Consequently, there is an ongoing, high level activity in this research field in order to build, to evaluate, and to recommend prediction techniques [7], [8], [9], [10], [11], [12] [13], [14]. A large number of different predictive models (estimation models and predictive models are considered synonyms) have been proposed over the last years.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

Tronto

Silva

Sant’Anna

2007

2007 International Joint Conference on Neural Networks

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Abstract-Good practices in software project management are basic requirements for companies to stay in the market, because the effective project management leads to improvements in product quality and cost reduction. Fundamental measurements are the prediction of size, effort, resources, cost and time spent in the software development process. In this paper, predictive Artificial Neural Network (ANN) and Regression based models are investigated, aiming at establishing simple estimation methods alternatives. The results presented in this paper compare the performance of both methods and show that artificial neural networks are effective in effort estimation.

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Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

Tronto

Silva

Sant’Anna

2007

2007 International Joint Conference on Neural Networks

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“…Another, more complex approach is given by Sentas et al [21] who use productivity and ordinal regression to assess the reliability of a given project cost classification.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Analysis of Software Productivity over Time

Premraj

Shepperd

Kitchenham

et al.

11th IEEE International Software Metrics Symposium (METRICS'05)

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OBJECTIVE -the aim is to investigate how software project productivity has changed over time. Within this overall goal we also compare productivity between different business sectors and seek to identify major drivers. METHOD -we analysed a data set of more than 600 projects that have been collected from a number of Finnish companies since 1978. RESULTS -overall, we observed a quite pronounced improvement in productivity over the entire time period, though, this improvement is less marked since the 1990s. However, the trend is not smooth. We also observed productivity variability between company and business sector. CONCLUSIONS -whilst this data set is not a random sample so generalisation is somewhat problematic, we hope that it contributes to an overall body of knowledge about software productivity and thereby facilitates the construction of a bigger picture.

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“…It comprises 145 maintenance and development projects undertaken between 1994 and 1998 by a single software development company. Maxwell's detailed description can be found in [38]. It contains 62 projects from one of the biggest commercial banks in Finland, covering the years 1985 to 1993 and both in-house and outsourced development.…”

Section: Kitchenmaxmentioning

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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Software productivity and effort prediction with ordinal regression

Cited by 106 publications

References 17 publications

Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation

An Empirical Analysis of Software Productivity over Time

Clustering Dycom

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