Issues on Estimating Software Metrics in a Large Software Operation

Barros, Rodrigo C.; Ruiz, Duncan D.; Tenório, Nelson; Basgalupp, Márcio P.; Becker, Karin

doi:10.1109/sew.2008.22

Cited by 6 publications

(3 citation statements)

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“…3; (b) continuing the effort to include a prediction component within the SPDW? architecture, based on data mining techniques (Barros et al 2008); (c) evolving the MP, organizational set of standard processes (OSSP), and product development process (PDP) through schemes, intending to assist business evolution and keeping the organization history; (d) investigating other SDP models in order to verify the SPDW? range of applicability; (e) testing the SPDW?…”

Section: Discussionmentioning

confidence: 99%

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SPDW+: a seamless approach for capturing quality metrics in software development environments

Silveira

Becker²,

Ruiz

2010

Software Qual J

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Among the key factors for the success of a metrics program are the regularity of metrics collection, a seamless and efficient data collection methodology, and the presence of non-intrusive automated data collection tools. This paper presents the software process data warehousing architecture SPDW? as a solution to the frequent, seamless, and automated capturing of software quality metrics, and their integration in a central repository for a full range of analyses. The striking features of the SPDW? ETL (data extraction, transformation, and loading) approach are that it addresses heterogeneity issues related to the software development context, it is automatable and non-intrusive, and it allows different capturing frequency and latency strategies, hence allowing both analysis and monitoring of software metrics. The paper also provides a reference framework that details three orthogonal dimensions for considering ETL issues in the software development process context, used to develop SPDW? ETL. The advantages of SPDW? are: (1) flexibility to meet the requirements of the frequent changes in SDP environments; (2) support for monitoring, which implies the execution of frequent and incremental loads; (3) automation of the complex and time-consuming task of capturing metrics, making it seamless; (4) freedom of choice regarding management models and support tools used in projects; and (5) cohesion and consistency of the information stored in the metrics repository which will be used to compare data of different projects. The paper presents the reference framework, illustrates the key role played by the metrics capturing process in a metrics program using a case study, and presents the striking features of SPDW? and its ETL approach, as well as an evaluation based on a prototype implementation.

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Section: Discussionmentioning

confidence: 99%

“…(e.g. Barros et al 2008), but have not considered this type of activity thoroughly. The remaining of this section describes how the SPDW?…”

Section: Spdw? Etl Benefitsmentioning

confidence: 99%

SPDW+: a seamless approach for capturing quality metrics in software development environments

Silveira

Becker²,

Ruiz

2010

Software Qual J

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“…An increasing number of machine learning approaches have been proposed and/or applied to predict software development (or maintenance) effort. Examples include case-based reasoning [10,17,20], artificial neural networks [5,6,19], decision trees [2,12,15], Bayesian networks [26,29], support vector machines for regression [11,27], genetic programming [23,31], and evolutionary algorithms in general [3,4].…”

Section: Introductionmentioning

confidence: 99%

A grammatical evolution approach for software effort estimation

Barros

Basgalupp

Cerri

et al. 2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.

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