Conversion from IFPUG FPA to COSMIC: Within-vs Without-Company Equations

Ferrucci, Filomena; Gravino, Carmine; Sarro, Federica

doi:10.1109/seaa.2014.76

Cited by 5 publications

(2 citation statements)

References 18 publications

(29 reference statements)

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“…-Experimenting with, and comparing, different size measures as cost drivers (e.g. [36], [73], [74], [75], [76]). -Experimenting with, and comparing, within-vs.cross company data (e.g [77], [78], [79], [80]).…”

Section: Related Workmentioning

confidence: 99%

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Sarro

Moussa

Petrozziello

et al. 2022

IIEEE Trans. Software Eng.

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In this paper, we introduce a novel approach to predictive modeling for software engineering, named Learning From Mistakes (LFM). The core idea underlying our proposal is to automatically learn from past estimation errors made by human experts, in order to predict the characteristics of their future misestimates, therefore resulting in improved future estimates. We show the feasibility of LFM by investigating whether it is possible to predict the type, severity and magnitude of errors made by human experts when estimating the development effort of software projects, and whether it is possible to use these predictions to enhance future estimations. To this end we conduct a thorough empirical study investigating 402 maintenance and new development industrial software projects. The results of our study reveal that the type, severity and magnitude of errors are all, indeed, predictable. Moreover, we find that by exploiting these predictions, we can obtain significantly better estimates than those provided by random guessing, human experts and traditional machine learners in 31 out of the 36 cases considered (86%), with large and very large effect sizes in the majority of these cases (81%). This empirical evidence opens the door to the development of techniques that use the power of machine learning, coupled with the observation that human errors are predictable, to support engineers in estimation tasks rather than replacing them with machine-provided estimates.

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Section: Related Workmentioning

confidence: 99%

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Sarro

Moussa

Petrozziello

et al. 2022

IIEEE Trans. Software Eng.

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“…Regarding the baselines, we used two constant models, as done in several previous studies (e.g., [24,66,74,75,76,77]). The first constant model, Mean-EFF, considers the mean of the previous 24 project efforts (namely, EF F o1 , ..., EF F oi−1 , EF F oi+1 , ..., EF F o25 ) as the predicted effort, for each observation o i in the dataset (for i = 1, ...25), while the second model, MedianEFF, exploits the median of the previous 24 project efforts.…”

Section: Baselinesmentioning

confidence: 99%

Assessing the effectiveness of approximate functional sizing approaches for effort estimation

Martino

Ferrucci

Gravino

et al. 2020

Information and Software Technology

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Context: Functional Size Measurement (FSM) methods, like Function Points Analysis (FPA) or COSMIC, are well-established approaches to estimate software size. Several approximations of these methods have been recently proposed as they require less time/information to be applied, however their effectiveness for effort prediction is not known. Objective: The effectiveness of approximated functional size measures for estimating the development effort is a key open question, since an approximate sizing approach may miss to capture factors affecting the effort. Therefore, we empirically investigated the use of approximate FPA and COSMIC sizing approaches, also compared with their standard versions, for effort estimation. Method: We measured 25 industrial software projects realised by a single company by using FPA, COSMIC, two approximate sizing approaches proposed by IFPUG for FPA (i.e. High Level and Indicative FPA), and three approximate sizing approaches proposed by the COSMIC organisation for COSMIC (i.e. Average Functional Process, Fixed Size Classification, and Equal Size Band). Then we investigated the quality of the regression models built using the obtained measures to estimate the development effort. Results: Models based on High Level FPA are effective, providing a prediction accuracy comparable to the one of the original FPA, while those based on the Indicative FPA method show poor estimation accuracy. Models based on COS-MIC approximate sizing methods are also quite effective, in particular those based on the Equal Size Band approximation provided an accuracy similar to the one of standard COSMIC. Conclusion: Project managers should be aware that predictions based on High Level FPA and standard FPA can be similar, making this approximation very interesting and effective, while Indicative FPA should be avoided. COSMIC approximations can also provide accurate effort estimates, nevertheless, the Fixed Size Classification and Equal Size Band approaches introduce subjectivity in

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From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation

Corazza¹,

Martino²,

Ferrucci

et al. 2015

Product-Focused Software Process Improvement

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Software companies exploit data about completed projects to estimate the development effort required for new projects. Software size is one of the most important information used to this end. However, different methods for sizing software exist and companies may require to migrate to a new method at a certain point. In this case, in order to exploit historical data they need to resize the past projects with the new method. Besides to be expensive, resizing is also often not possible due to the lack of adequate documentation. To support size measurement migration, we propose a transfer learning approach that allows to avoid resizing and is able to estimate the effort of new projects based on the combined use of data about past projects measured with the previous measurement method and projects measured with the new one. To assess our proposal, an empirical analysis is carried out using an industrial dataset of 25 projects. Function Point Analysis and COSMIC are the measurement methods taken into account in the study

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Conversion from IFPUG FPA to COSMIC: Within-vs Without-Company Equations

Cited by 5 publications

References 18 publications

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Assessing the effectiveness of approximate functional sizing approaches for effort estimation

From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation

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