Effort Prediction Model Using Similarity for Embedded Software Development

Iwata, Kazunori; Anan, Yoshiyuki; Nakashima, Toyoshiro; Ishii, Naohiro

doi:10.1007/11751588_5

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…This requires improving further the quality and cost during software development. Hence, we have already studied costs of the processes by using analysis (Iwata et al (2006b); Nakashima et al (2006)) and collaborative filtering (Iwata et al (2006a)). To cope with this situation, the tools that can manage the progress status or results in the database are used to improve the quality and productivity.…”

Section: Software Project Management and Issuesmentioning

confidence: 99%

“…However, there is little research on the relationship between the scale of the development and the number of errors, based of data accumulated from past projects. As a result, previously we described the prediction of the total scale using multiple regression analysis (Iwata et al (2006b); Nakashima et al (2006)) and collaborative filtering (Iwata et al (2006a)). In this Chapter we therefore, propose a method for creating effort and errors prediction model using an Artificial Neural Network (ANN) for complementing missing values (Iwata et al (2006a)).…”

Section: Introductionmentioning

confidence: 99%

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Applying an Artificial Neural Network to Predicting Effort and Errors for Embedded Software Development Projects

Iwata¹,

Nakashima²,

Anan³

et al. 2011

Artificial Neural Networks - Application

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Section: Software Project Management and Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying an Artificial Neural Network to Predicting Effort and Errors for Embedded Software Development Projects

Iwata¹,

Nakashima²,

Anan³

et al. 2011

Artificial Neural Networks - Application

Self Cite

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“…Iwata et al propose a top-30 multiple regression method to predict effort for embedded software development [37]. The use a collaborative filtering system to generate the similarity scores between projects.…”

Section: Top-k Regressionmentioning

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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