A Pattern Recognition Approach for Software Engineering Data Analysis

Lionel C. Briand; Victor R. Basili; William M. Thomas

doi:10.1109/32.177363

A Pattern Recognition Approach for Software Engineering Data Analysis

Lionel C. Briand, Victor R. Basili, William M. Thomas

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

In order to plan, control, and evaluate the software development process, one needs to collect and analyze data in a meaningful way. Classical techniques for such analysis are not always well suited to software engineering data. In this paper we describe a pattern recognition approach for analyzing software engineering data, called optimized set reduction (OSR), that addresses many of the problems associated with the usual approaches. Methods are discussed for using the technique for prediction, risk management, and quality evaluation. Experimental results are provided to demonstrate the effectiveness of the technique for the particular application of software cost estimation.

Original language	English
Pages (from-to)	931-942
Number of pages	12
Journal	IEEE Transactions on Software Engineering
Volume	18
Issue number	11
DOIs	https://doi.org/10.1109/32.177363
Publication status	Published - Nov 1992
Externally published	Yes

Keywords

Classification
data analysis
empirical modeling
machine learning
pattern recognition
quality evaluation
risk assessment
software development cost prediction
stochastic modeling

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10.1109/32.177363

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KW - Classification

KW - data analysis

KW - empirical modeling

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KW - quality evaluation

KW - risk assessment

KW - software development cost prediction

KW - stochastic modeling

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A Pattern Recognition Approach for Software Engineering Data Analysis

Abstract

Keywords

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