A comparison of development effort estimation techniques for Web hypermedia applications

Mendes, Emilia; Watson, Ian; Triggs, C.M.; Mosley, Nile; Counsell, Steve

doi:10.1109/metric.2002.1011332

Cited by 76 publications

(60 citation statements)

References 26 publications

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“…The increasing growth and complexity of software systems today has pushed management into adopting more structured and informed ways of estimating development effort, namely prediction models. Many prediction models have been developed in an attempt to improve development effort estimation, each with its own slightly different take on the software process model [11,14,25,41,44,53,80]. Due to the complexity of the software development process, thus far no one model can address all of the variability involved.…”

Section: Thesis Organizationmentioning

confidence: 99%

An effort prediction framework for software defect correction

Hassouna¹,

Tahvildari²

2010

Information and Software Technology

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Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modified code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the effort required to correct them. Although software development effort prediction has been in use for many years, research into defect-correction effort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this field. Effort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources effectively and efficiently. In particular, early defect-correction effort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases.In this work, we address the problem of predicting the effort needed to resolve a software defect. More specifically, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce effort predictions. This approach usually suffers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are affected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known effort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the effort, where the effect of such inconsistencies can be reduced.We have performed a number of experimental studies on the proposed framework to assess the effectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity...

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Section: Thesis Organizationmentioning

confidence: 99%

An effort prediction framework for software defect correction

Hassouna¹,

Tahvildari²

2010

Information and Software Technology

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“…The rest of the papers consists of 12 case studies [20][21][22][23][24][25][26][27][28][29][30][31], 11 reviews [32][33][34][35][36][37][38][39][40][41][42], 11 empirical analyzes [43][44][45][46][47][48][49][50][51][52][53], three comparative analyzes ( [54][55][56]) and two field studies [57,58].…”

Section: Introductionmentioning

confidence: 99%

Rethinking Experiments in a Socio-Technical Perspective: The Case of Software Engineering

Schiaffonati

Verdicchio

2015

Philosophies

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Experiments in computing share many characteristics with the traditional experimental method, but also present significant differences from a practical perspective, due to their aim at producing software artifacts and the central role played by human actors and organizations (e.g., programmers, project teams, software houses) involved in the software development process. By analyzing some of the most significant experiments in the subfield of software engineering, we aim at showing how the conceptual framework that supports experimental methodology in this context needs an extension in a socio-technical perspective.

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“…They estimated effort using machine-learning techniques with data from student-based Web projects, and size measures harvested late in the project's life cycle. Mendes and collaborators also carried out a series of consecutive studies [13], [26]- [36] building models using multivariate regression and machine-learning techniques using data on student-based and industrial Web projects. Recently Mendes [25] investigated the use of Bayesian Networks for Web effort estimation, using data on industrial Web projects from the Tukutuku database.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We used a commercial CBR tool -CBR-Works from tec:inno, to obtain effort estimates and the choice of parameters used in this study was motivated by previous studies that applied CBR for Web effort estimation [8], [27], [28], [29], [32]- [36], and to some extent, on the CBR tool employed:…”

Section: Building the Case-based Reasoning Predictionsmentioning

confidence: 99%

“…Since CBR-Works does not provide a feature subset selection mechanism [43], we decided to use only those features significantly associated with TotEff [8], [28], [36]. Associations between numerical variables and TotEff were measured using a nonparametric test, the Spearman's rank correlation test; the associations between numerical and categorical variables were checked using the one-way ANOVA test.…”

Section: Building the Case-based Reasoning Predictionsmentioning

confidence: 99%

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