A PATTERN-RECOGNITION APPROACH FOR SOFTWARE ENGINEERING DATA-ANALYSIS

被引：109

作者：

BRIAND, LC ^{[1
]}

BASILI, VR ^{[1
]}

THOMAS, WM ^{[1
]}

机构：

[1] UNIV MARYLAND,DEPT COMP SCI,COLL PK,MD 20742

来源：

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | 1992年 / 18卷 / 11期

关键词：

CLASSIFICATION; DATA ANALYSIS; EMPIRICAL MODELING; MACHINE LEARNING; PATTERN RECOGNITION; QUALITY EVALUATION; RISK ASSESSMENT; SOFTWARE DEVELOPMENT COST PREDICTION; STOCHASTIC MODELING;

D O I：

10.1109/32.177363

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：931 / 942

页数：12