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Estimating t-way Fault Profile Evolution During Testing
D Richard Kuhn1, Raghu N Kacker1, Yu Lei2
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Proceedings : Annual International Computer Software and Applications Conference. COMPSAC
|November 15, 2024
Summary
Most software interaction faults involve few variables. This study hypothesizes why this occurs, offering insights into fault removal and software reliability growth.
Area of Science:
- Computer Science
- Software Engineering
- Reliability Engineering
Background:
- Empirical studies reveal a pattern in software interaction faults.
- Most faults involve one or two interacting variables.
- Failures involving more than six variables are unreported.
Purpose of the Study:
- To propose a hypothesis explaining the observed distribution of software interaction faults.
- To explore implications for software fault removal strategies.
- To enhance understanding of software reliability growth.
Main Methods:
- Analysis of existing empirical data on software interaction faults.
- Development of a theoretical hypothesis for fault distribution.
- Discussion of the hypothesis's implications for fault removal and reliability.
Main Results:
- A hypothesis is presented to explain the prevalence of low-variable interaction faults.
- The findings suggest potential improvements in identifying and removing interaction faults.
- The study provides a framework for understanding reliability growth based on fault interaction.
Conclusions:
- The proposed hypothesis offers a novel perspective on software fault behavior.
- Understanding the origin of fault distributions can lead to more effective testing and debugging.
- This work contributes to advancing the field of software reliability engineering.
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