Semester
Spring
Date of Graduation
2001
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Ali Mili.
Abstract
Whether and when to adapt to certain software engineering trends are difficult questions to be answered by many decision-makers. The main reasons are due to the fact that evolution of software engineering trends itself is determined by various factors, many of which come from the fields outside of the software technology, thus hard to predict. So it is even harder to estimate the cost and benefit when adapting to certain trends. This paper is intended to study ways to decrease the risk involved in such decision making processes, by developing a pattern from past software engineering trends. While the pattern cannot answer all the questions by itself, it can relief the decision makers in a large extent by providing the most important information relevant to the software engineering trends. The pattern recognition is achieved by using neural networks. Our result seems to be very encouraging, which begins to prove that there does exist pattern between the input data that we can observe and the output data that we need to know. Although more trends need to be observed and analyzed before we can reach a more concrete conclusion, it does show that neural network may be a valid approach in future research.
Recommended Citation
Chen, Dapeng, "Pattern recognition in software engineering trend adapting" (2001). Graduate Theses, Dissertations, and Problem Reports. 1246.
https://researchrepository.wvu.edu/etd/1246