Analyzing Error-Prone System Structure
IEEE Transactions on Software Engineering
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Lessons Learned in Building a Corporate Metrics Program
IEEE Software
Software Measurement: A Necessary Scientific Basis
IEEE Transactions on Software Engineering
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Object-oriented metrics: A review of theory and practice
Advances in software engineering
A Methodology for Architecture-Level Reliability Risk Analysis
IEEE Transactions on Software Engineering
Return on Investment of Software Quality Predictions
ASSET '98 Proceedings of the 1998 IEEE Workshop on Application - Specific Software Engineering and Technology
Integrating metrics and models for software risk assessmen
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Classification Tree Models of Software Quality Over Multiple Releases
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Source code-based software risk assessing
Proceedings of the 2005 ACM symposium on Applied computing
PRIMES: an ontology-based web service for software risk management
International Journal of Business Information Systems
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Abstract: ARMOR (Analyzer for Reducing Module Operational Risk) is a software risk analysis tool which automatically identifies the operational risks of software program modules. ARMOR takes data directly from project database, failure database, and program development database, establishes risk models according to several risk analysis schemes, determines the risks of software programs, and displays various statistical quantities for project management and engineering decisions. Its enhanced user interface greatly simplifies the risk modeling procedures and the usage learning time. The tool can perform the following tasks during project development, testing, and operation: establish promising risk models for the project under evaluation; measure the risks of software programs within the project; identify the source of risks and indicate how to improve software programs to reduce their risk levels; and determine the validity of risk models from field data.