Bayesian analysis of software cost and quality models

Chulani, Sunita

doi:10.1109/icsm.2001.972773

Cited by 24 publications

(25 citation statements)

References 4 publications

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“…However, early in the preparation stage of the investigation it became apparent that such an approach was unfeasible: either the necessary defect data for determining defect root causes is unavailable, or organizations refuse to provide them. A similar observation was reported by Chulani [6]. Moreover, a quick scan shows that organizations limit the extent of defect classification and analysis by only collecting defect data for solving problems at hand.…”

Section: Problem Areas Of Virtual Development As Basis For Defect Causupporting

confidence: 59%

Exploring defect causes in products developed by virtual teams

Jacobs

Moll

Krause

et al. 2005

Information and Software Technology

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Section: Problem Areas Of Virtual Development As Basis For Defect Causupporting

confidence: 59%

Exploring defect causes in products developed by virtual teams

Jacobs

Moll

Krause

et al. 2005

Information and Software Technology

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“…Another example of process support for aspects of Dependability is the means-ends summary of process choices for software defect detection and removal used in the Constructive Quality Model (COQUALMO) 33,34 . In concert with the COCOMO II software cost model 34 , it identifies different levels of investment in software defect detection and removal via automated analysis, peer reviews, and execution testing and tools, and produces resulting estimates of delivered defect density in terms of number of defects per thousand lines of code, along with associated costs and returns on investment via the Information Dependability and Value Estimation (iDAVE) model 35 .…”

Section: Processesmentioning

confidence: 99%

An Initial Ontology for System Qualities

Boehm

Kukreja

2015

INCOSE International Symp

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Abstract. This paper presents an initial ontology for reasoning about a system's System Qualities (SQs), ilities, or non-functional requirements (reliability, usability, affordability, etc.). The need for such an ontology is based primarily on two factors. One is the importance of getting the SQs sufficiently well defined that the system's definition, development and evolution result in a satisfactory balance of SQ values for the system's success-critical stakeholders, given the frequent system shortfalls and overruns that occur when this balance is not achieved. The other is that current system acquisition and evolution guidance descriptions have numerous deficiencies and inconsistencies in their coverage of SQ considerations. This situation is becoming more serious as systems and their stakeholders become increasingly complex, dynamic, and diverse. This paper provides an elaboration of the needs, a set of initial SQ ontology elements and definitions, examples of their application to some key SQs and their relationships, and an identification of further research and development needed to make the ontology fully useful and evolvable.

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“…Uncertainty also comes from the consideration of human factors such as the ability and expertise of both the development team and the managers. Thus, there have been several approaches to software estimation that use models of uncertainty, such as Bayesian Belief Networks (BBNs), 40 a direct use of Bayes theorem, 14 or fuzzy logic. 1 In this paper we also propose the use of BBNs.…”

Section: Software Project Estimationsmentioning

confidence: 99%

Multi-step Generation of Bayesian Networks Models for Software Projects Estimations

Fuentetaja¹,

Borrajo²,

López³

et al. 2013

IJCIS

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Software projects estimations are a crucial component of successful software development. There have been many approaches that deal with this problem by using different kinds of techniques. Most of the successful techniques rely on one shot prediction of some variables, as cost, quality or risk, taking into account some metrics. However, these techniques usually are not able to deal with uncertainty on the data, the relationships among metrics or the temporal aspect of projects. During the last decade, some researchers have proposed the use of Bayesian Belief Networks (BBNs) to perform better estimations, by explicitly taking into account the previous shortcomings. But, these approaches were based on manually defining those BBNs and handling only one of the estimation variables (cost, quality or risk). In this paper, we present an approach for semi-automatically building BBNs by using machine learning techniques. We describe two algorithms to generate such BBNs. The first one generates one-shot BBNs, while the second one generates BBNs that take into account the temporal aspect of project development. We performed experiments on real data coming from two software companies, obtaining a 63% of accuracy on multiclass classification. Our main interest was to find a semantically correct model that can be trained with future projects to increase its accuracy. In this sense, we introduce a well-balanced approach to make good predictions with strong explanatory power.

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Bayesian analysis of software cost and quality models

Cited by 24 publications

References 4 publications

Exploring defect causes in products developed by virtual teams

Exploring defect causes in products developed by virtual teams

An Initial Ontology for System Qualities

Multi-step Generation of Bayesian Networks Models for Software Projects Estimations

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