Systematic literature review of machine learning based software development effort estimation models

Wen, Jianfeng; Li, Shixian; Zhang, Lin; Hu, Yong; Huang, Changqin

doi:10.1016/j.infsof.2011.09.002

Cited by 437 publications

(370 citation statements)

References 102 publications

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“…No campo das ciências sociais, este tipo de procedimento é relativamente recente (Velamuri, Neyer, & Möslein, 2011). Todavia algumas pesquisas podem ser destacadas: sobre o GCS (Wong, Tjosvold, & Zhang, 2005); sobre a seleção de fornecedores para terceirização (Khan, Niazi, & Ahmad, 2011); sobre a seleção de fornecedores de software (Wen, Li, Lin, Hu, & Huang, 2012). Cronin et al (2008) propuseram um protocolo de revisão sistemática baseado naquele utilizado na área de medicina e proposto por Higgins e Green (2011), envolvendo as etapas: (a) formulação da questão de pesquisa; (b) conjunto de critérios de inclusão e exclusão; (c) seleção e acesso da literatura; (d) avaliação da qualidade da literatura incluída na revisão; e, (e) análise, síntese e disseminação dos resultados.…”

Section: Procedimento Metodológico Utilizado Na Pesquisaunclassified

Síntese dos Principais Critérios, Métodos e Subproblemas da Seleção de Fornecedores Multicritério

Guarnieri

2015

Rev. adm. contemp.

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O objetivo do presente artigo é identificar na literatura quais são os principais critérios considerados no processo de decisão, os principais métodos individuais e combinados e os subproblemas da seleção de fornecedores no contexto multicritério. Cabe ressaltar que as tarefas de avaliar e identificar os melhores fornecedores para um novo produto ou serviço são essenciais ao gerenciamento da cadeia de suprimentos (GCS). Para executá-las apropriadamente, considerando-se a natureza multicritério da seleção de fornecedores, torna-se necessária a inclusão de uma abordagem de agregação dos critérios, cuja escolha depende basicamente dos objetivos da decisão, tipos de critérios a serem utilizados e racionalidade do decisor. Para atingir o objetivo proposto, optou-se por utilizar o procedimento da revisão sistemática da literatura que segue o protocolo proposto por Cronin, Ryan e Coughlan (2008), abrangendo artigos publicados nas bases ISI Web of Knowledge e Scopus, no período de 2001 a 2012. Além de sintetizar o conhecimento acerca do tema, o artigo ressalta os principais critérios, métodos e subproblemas da seleção de fornecedores, com isso, contribui ao identificar os aspectos que devem ser desenvolvidos em estudos futuros e ao apresentar aos gestores abordagens existentes para a tomada de decisões relativa à seleção de fornecedores de forma sistematizada.

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Section: Procedimento Metodológico Utilizado Na Pesquisaunclassified

Síntese dos Principais Critérios, Métodos e Subproblemas da Seleção de Fornecedores Multicritério

Guarnieri

2015

Rev. adm. contemp.

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“…Sometimes, software engineers may not improve their effort estimations even after feedback about their estimates is provided (Gruschke and Jørgensen 2008). Therefore, several Machine Learning (ML) approaches have been investigated to automatically build SEE models (Wen et al 2012). However, SEE models created using ML might not capture some human factors that influence the effort required to develop software projects.…”

Section: Introductionmentioning

confidence: 99%

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by A preliminary version of this work appeared as Minku and Yao (2012a

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“…SDEE includes software development and maintenance efforts. In software engineering researches, cost and effort estimation are used equivalently [1][2][3][4]. Accurate estimation of development cost has an important role in the success or failure of a software project.…”

Section: Introductionmentioning

confidence: 99%

“…Where the costs or efforts are calculated as numeric values in software projects, regression methods are studied as the ML models in SDEE researches. The ML algorithms in SDEE are divided into 6 categories [3] as case base reasoning (CBR) [12], artificial neural networks (ANNs) [14], decision trees (DT) [15], Bayesian networks (BN) [16], support vector regression (SVR) [14], and association rules (AR) [17]. Searching for useful and effective subset of features is known as the approach in the ML area to increase the learning model accuracy.…”

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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Systematic literature review of machine learning based software development effort estimation models

Cited by 437 publications

References 102 publications

Síntese dos Principais Critérios, Métodos e Subproblemas da Seleção de Fornecedores Multicritério

Síntese dos Principais Critérios, Métodos e Subproblemas da Seleção de Fornecedores Multicritério

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

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

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