DocumentCode :
3259781
Title :
Extracting procedural knowledge from software systems using inductive learning in the PM system
Author :
Reynolds, Robert G. ; Maletic, Jonathan I.
Author_Institution :
Dept. of Comput. Sci., Wayne State Univ., Detroit, MI
fYear :
1992
fDate :
15-20 Jun 1992
Firstpage :
131
Lastpage :
139
Abstract :
The issue of software reuse has been found to be a much harder task than previously thought. Some of the problems are due to the lack of emphasis placed on non-functional requirements during the software development phase, such as maintainability and understandability. Other problems arise from the difficulty of defining precise criteria for considering a software module reusable. They are usually elusive, and vary dramatically from one domain to another. This paper presents PM, a software system the goal of which is the automation of the software reuse process. PM uses an incremental approach in performing analysis and storage of software modules, at different levels of granularity. Its fundamental characteristics are domain independence and flexibility, accomplished applying inductive learning techniques and analyzing reusable and nonreusable code examples
Keywords :
knowledge acquisition; knowledge based systems; learning (artificial intelligence); software reusability; PM system; Partial Metrics system; granularity; inductive learning; procedural knowledge acquisition; software development; software maintenance; software reuse; Automation; Performance analysis; Programming; Software performance; Software reusability; Software systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering and Knowledge Engineering, 1992. Proceedings., Fourth International Conference on
Conference_Location :
Capri
Print_ISBN :
0-8186-2830-8
Type :
conf
DOI :
10.1109/SEKE.1992.227937
Filename :
227937
Link To Document :
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