An experimental evaluation of the assumption of independence in multiversion programming
IEEE Transactions on Software Engineering
Axiomatizing software test data adequacy
IEEE Transactions on Software Engineering
The effects of optimizing transformations on data-flow adequate test sets
TAV4 Proceedings of the symposium on Testing, analysis, and verification
A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
On the Expected Number of Failures Detected by Subdomain Testing and Random Testing
IEEE Transactions on Software Engineering
Handbook of software reliability engineering
Handbook of software reliability engineering
Effect of test set minimization on fault detection effectiveness
Software—Practice & Experience
Empirical Studies of a Safe Regression Test Selection Technique
IEEE Transactions on Software Engineering
Test set size minimization and fault detection effectiveness: a case study in a space application
Journal of Systems and Software
Test case selection with and without replacement
Information Sciences—Informatics and Computer Science: An International Journal
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Reliability prediction for component-based software architectures
Journal of Systems and Software - Special issue on: Software architecture - Engineering quality attributes
Mutation analysis of program test data
Mutation analysis of program test data
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Is mutation an appropriate tool for testing experiments?
Proceedings of the 27th international conference on Software engineering
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
A concept analysis inspired greedy algorithm for test suite minimization
PASTE '05 Proceedings of the 6th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Failure proximity: a fault localization-based approach
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Enhancing adaptive random testing in high dimensional input domains
Proceedings of the 2007 ACM symposium on Applied computing
On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques
IEEE Transactions on Software Engineering
Statistical Debugging: A Hypothesis Testing-Based Approach
IEEE Transactions on Software Engineering
Proceedings of the 2007 international symposium on Software testing and analysis
An upper bound on software testing effectiveness
ACM Transactions on Software Engineering and Methodology (TOSEM)
A Crosstab-based Statistical Method for Effective Fault Localization
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
Using an RBF Neural Network to Locate Program Bugs
ISSRE '08 Proceedings of the 2008 19th International Symposium on Software Reliability Engineering
A family of code coverage-based heuristics for effective fault localization
Journal of Systems and Software
Insights on Fault Interference for Programs with Multiple Bugs
ISSRE '09 Proceedings of the 2009 20th International Symposium on Software Reliability Engineering
Using Mutation to Automatically Suggest Fixes for Faulty Programs
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Combining mutation and fault localization for automated program debugging
Journal of Systems and Software
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Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.