A Replicated Comparison of Cross-Company and Within-Company Effort Estimation Models Using the ISBSG Database

Mendes, Emilia; Lokan, Chris; Harrison, Rachel; Triggs, C.M.

doi:10.1109/metrics.2005.4

Cited by 45 publications

(77 citation statements)

References 17 publications

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“…The ISBSG repository provides organizations with a broad range of project data from various industries and business areas [Hil11]. The data can be used for effort estimation, trend analysis, comparison of platforms and languages, and productivity benchmarking [MLHT05]. The ISBSG repository is a multi-organizational, multi-application, and multi-environment data repository [CA13].…”

Section: Changes To the Original Studymentioning

confidence: 99%

Function Point Structure and Applicability: A Replicated Study.

López¹,

Coronas²

2016

JOT

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Background: The complexity of providing accurate functional software size and effort prediction models is well known in the software industry. Function point analysis (FPA) is currently one of the most accepted software functional size metrics in the industry, but it is hardly automatable and generally requires a lengthy and costly process. Objectives: This paper reports on a family of replications carried out on a subset of the International Software Benchmarking Standards Group dataset (ISBSG R12) to evaluate the structure and applicability of function points. The goal of this replication is to aggregate evidence about internal issues of FPA as a metric, and to confirm previous results using a different set of data. Methods: A subset of 202 business application projects from 2005 to 2011 was analyzed. FPA counting was analyzed in order to determine the extent to which the basic functional components (BFC) were independent of each other and thus appropriate for an additive model of size. The correlations among effort and BFCs and unadjusted function points (UFP) were assessed in order to determine whether a simplified sizing metric might be appropriate to simplify effort prediction models. Prediction models were constructed and evaluated in terms of accuracy. Results: The results confirmed that some BFCs of the FPA method are correlated. There is a relationship between BFCs and effort. That suggest that prediction models based on transactional functions (TF) or external inputs (EI) appears to be as good as a model based on UFP in this subset of projects. Conclusions: The results might suggest an improvement in the performance of the measurement process. Simplifying the FPA measurement process based on counting a subset of BFCs could allow savings in measurement effort, preserving the accuracy of effort estimates.

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Section: Changes To the Original Studymentioning

confidence: 99%

Function Point Structure and Applicability: A Replicated Study.

López¹,

Coronas²

2016

JOT

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“…We analyzed the performance of effort prediction models based on base functional components and unadjusted function point size (Albrecht 1979;Jeng et al 2011). The subset of data projects for our study was selected according to the criteria shown in Table 6 based on recommendations presented in (Dejaeger et al 2012;Murillo-Morera et al 2016a;Mendes et al 2005;Mendes and Lokan 2008;Seo et al 2013;Quesada-López and Jenkins 2014;2015;. Projects for which all functional components (UFP and BFC) of function points were missing were discarded.…”

Section: Dataset Selectionmentioning

confidence: 99%

A genetic algorithm based framework for software effort prediction

Murillo-Morera

Quesada-López

Castro-Herrera

et al. 2017

J Softw Eng Res Dev

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Background: Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models. Objectives: In this study, we validate an automated genetic framework, and then conduct a sensitivity analysis across different genetic configurations. Following is the comparison of the framework with a baseline random guessing and an exhaustive framework. Lastly, we investigate the performance results of the best learning schemes. Methods: In total, six hundred learning schemes that include the combination of eight data preprocessors, five attribute selectors and fifteen modeling techniques represent our search space. The genetic framework, through the elitism technique, selects the best learning schemes automatically. The best learning scheme in this context means the combination of data preprocessing + attribute selection + learning algorithm with the highest coefficient correlation possible. The selected learning schemes are applied to eight datasets extracted from the ISBSG R12 Dataset. Results: The genetic framework performs as good as an exhaustive framework. The analysis of the standardized accuracy (SA) measure revealed that all best learning schemes selected by the genetic framework outperforms the baseline random guessing by 45-80%. The sensitivity analysis confirms the stability between different genetic configurations. Conclusions: The genetic framework is stable, performs better than a random guessing approach, and is as good as an exhaustive framework. Our results confirm previous ones in the field, simple regression techniques with transformations could perform as well as nonlinear techniques, and ensembles of learning machines techniques such as SMO, M5P or M5R could optimize effort predictions.

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“…The effect of backward selection on each ML algorithm was studied in this work. Datasets in SDEE were divided into the within-company and cross-company categories [20][21][22]. In [34], the FS effect has been investigated to SDEE within-company and crosscompany datasets, and it has been concluded that cost estimation with less features provides methods.…”

Section: Introductionmentioning

confidence: 99%

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

Beiranvand

Chahooki

2016

JAIDM

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Software project management is one of the significant activates in the software development process. Software development effort estimation (SDEE) is a challenging task in the software project management. SDEE has been an old activity in computer industry from 1940s, and thus it has been reviewed for several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before a software project contract. Due to the uncertain nature of development estimates, and in order to increase the accuracy, researchers have recently focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this work, for narrowing the semantic gap in SDEE, a hierarchical filter and wrapper feature selection (FS) techniques and fused measurement criteria are developed in a two-phase approach. In the first phase, the two-stage filter FS methods provide start sets for the wrapper FS techniques. In the second phase, a fused criterion is proposed to evaluate the accuracy in wrapper FS techniques. The experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.

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A Replicated Comparison of Cross-Company and Within-Company Effort Estimation Models Using the ISBSG Database

Cited by 45 publications

References 17 publications

Function Point Structure and Applicability: A Replicated Study.

Function Point Structure and Applicability: A Replicated Study.

A genetic algorithm based framework for software effort prediction

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

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