Abstract
Cognitive learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple learning effects in the models simultaneously to discuss the influence of learning effects on work performance in software debugging projects and also the influence of variations in the environment on learning effects. The models are based on Chiu's models [2] and employed a sine function [15] to describe the variable potential errors and to judge change-points in the software debugging process. The results showed almost perfect fitting for the models to the actual data sets, which means the staff engaged in software debugging projects have not only conscious learning effects but also unconscious behavior that can describe variable potential errors and explain the change-points in a software debugging process. This paper also examines the effectiveness of the proposed models and discuss when and what kinds of learning effects occur and how these influence software reliability those help managers to master the software debugging process, the performance of the staff involved in this process, the reliability of the software system, and in the employment of suitable methods to manage organizations.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Industrial Engineering and Engineering Management |
| Publisher | IEEE Computer Society |
| Pages | 1656-1660 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479909865 |
| DOIs | |
| Publication status | Published - 2014 Nov 18 |
| Event | 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 - Bangkok, Thailand Duration: 2013 Dec 10 → 2013 Dec 13 |
Publication series
| Name | IEEE International Conference on Industrial Engineering and Engineering Management |
|---|---|
| ISSN (Print) | 2157-3611 |
| ISSN (Electronic) | 2157-362X |
Other
| Other | 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 |
|---|---|
| Country | Thailand |
| City | Bangkok |
| Period | 13-12-10 → 13-12-13 |
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All Science Journal Classification (ASJC) codes
- Business, Management and Accounting (miscellaneous)
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality
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A discussion of multiple learning effects and unconscious behavior in the software debugging process with variable potential errors and change-points. / Chiu, Kuei Chen; Hsieh, Shulan.
IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society, 2014. p. 1656-1660 6962691 (IEEE International Conference on Industrial Engineering and Engineering Management).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A discussion of multiple learning effects and unconscious behavior in the software debugging process with variable potential errors and change-points
AU - Chiu, Kuei Chen
AU - Hsieh, Shulan
PY - 2014/11/18
Y1 - 2014/11/18
N2 - Cognitive learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple learning effects in the models simultaneously to discuss the influence of learning effects on work performance in software debugging projects and also the influence of variations in the environment on learning effects. The models are based on Chiu's models [2] and employed a sine function [15] to describe the variable potential errors and to judge change-points in the software debugging process. The results showed almost perfect fitting for the models to the actual data sets, which means the staff engaged in software debugging projects have not only conscious learning effects but also unconscious behavior that can describe variable potential errors and explain the change-points in a software debugging process. This paper also examines the effectiveness of the proposed models and discuss when and what kinds of learning effects occur and how these influence software reliability those help managers to master the software debugging process, the performance of the staff involved in this process, the reliability of the software system, and in the employment of suitable methods to manage organizations.
AB - Cognitive learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple learning effects in the models simultaneously to discuss the influence of learning effects on work performance in software debugging projects and also the influence of variations in the environment on learning effects. The models are based on Chiu's models [2] and employed a sine function [15] to describe the variable potential errors and to judge change-points in the software debugging process. The results showed almost perfect fitting for the models to the actual data sets, which means the staff engaged in software debugging projects have not only conscious learning effects but also unconscious behavior that can describe variable potential errors and explain the change-points in a software debugging process. This paper also examines the effectiveness of the proposed models and discuss when and what kinds of learning effects occur and how these influence software reliability those help managers to master the software debugging process, the performance of the staff involved in this process, the reliability of the software system, and in the employment of suitable methods to manage organizations.
UR - http://www.scopus.com/inward/record.url?scp=84914171234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84914171234&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2013.6962691
DO - 10.1109/IEEM.2013.6962691
M3 - Conference contribution
AN - SCOPUS:84914171234
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 1656
EP - 1660
BT - IEEE International Conference on Industrial Engineering and Engineering Management
PB - IEEE Computer Society
ER -