Software errors and complexity: an empirical investigation0
Communications of the ACM
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Predicting the cost-effectiveness of regression testing strategies
SIGSOFT '96 Proceedings of the 4th ACM SIGSOFT symposium on Foundations of software engineering
Using Coverage Information to Predict the Cost-Effectiveness of Regression Testing Strategies
IEEE Transactions on Software Engineering
Software architecture in practice
Software architecture in practice
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Empirical Studies of a Prediction Model for Regression Test Selection
IEEE Transactions on Software Engineering
The distribution of faults in a large industrial software system
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Software performance testing based on workload characterization
WOSP '02 Proceedings of the 3rd international workshop on Software and performance
Metrics to Assess the Likelihood of Project Success Basedon Architecture Reviews
Empirical Software Engineering
Reexamining the Fault Density-Component Size Connection
IEEE Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Difficulties Measuring Software Risk in an Industrial Environment
DSN '01 Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS)
Investigating Metrics for Architectural Assessment
METRICS '98 Proceedings of the 5th International Symposium on Software Metrics
Predicting Project Risk from Architecture Reviews
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
A Metric to Predict Software Scalability
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
| Hi-index | 0.00 |
An argument is made that predictive metrics provide a very powerful means for organizations to assess characteristics of their software systems and allow them to make critical decisions based on the value computed. Five different predictors are discussed aimed at different stages of the software lifecycle ranging from a metric that is based on an architecture review which is done at the earliest stages of development, before even low-level design has begun, to one designed to predict the risk of releasing a system in its current form. Other predictors discussed include the identification of characteristics of files that are likely to be particularly fault-prone, a metric to help a tester charged with regression testing to determine whether or not a particular selective regression testing algorithm is likely to be cost effective to run on a given software system and test suite, and a metric to help determine whether a system is likely to be able to handle a significantly increased workload while maintaining acceptable performance levels.