1. Developed by Reneta Barneva, SUNY Fredonia Product Metrics for Software

2. Developed by Reneta Barneva, SUNY Fredonia Definition Conformance to explicitly stated –Functional and performance requirements, –explicitly documented development standards, – and implicit characteristics that are expected of all professionally developed software.

3. Developed by Reneta Barneva, SUNY Fredonia McCall’s Quality Factors We can see McCall uses 11 different categories to summarize quality software.

4. Developed by Reneta Barneva, SUNY Fredonia ISO 9126 Standards The quality factors of ISO 9126 standards is a stripped down version of McCall’s factors Functionality Reliability Usability Efficiency Maintainability Portability

5. Developed by Reneta Barneva, SUNY Fredonia Quantitative Approach Product metrics use a quantitative approach, creating strict guidelines for measuring the quality of the product.

6. Developed by Reneta Barneva, SUNY Fredonia Framework for Product Metrics Split into measures, metrics and indicators Provides many “challenges” Measurement principles Attributes of effective software metrics The product metrics landscape

7. Developed by Reneta Barneva, SUNY Fredonia Metrics for the Analysis Model Function based metrics –External Outputs –External Inputs –External inquiries –Internal logical files –External interface files Each multiplied by a weighting factor depending on the implied complexity of the product

8. Developed by Reneta Barneva, SUNY Fredonia Metrics for the Design Model The U.S. Air Force Systems Command developed a number of software quality indicators similar to that of the IEEE Std. 982.1-1988. The design structure quality index (DSQI) rages from 0 to 1 with the values given by S1-S7 –S1 – The total number of modules defined in the program architecture –S2 – The number of modules whose correct function depends on the source of data input or that produce data to be used elsewhere –S3 – The number of modules whose correct function depends on prior processing –S4 – The number of modules whose correct function depends on prior processing –S5 – The total number of unique database items –S6 – The number of database segments –S7 – The number of modules with a single entry and exit

9. Developed by Reneta Barneva, SUNY Fredonia Metrics for Source Code Halstead metrics – provide a unique measure of a computer program Complexity metrics – Measure the logical complexity of source code Length metrics – Provide an indication of the size of the software

10. Developed by Reneta Barneva, SUNY Fredonia Metrics for Testing Statement and branch coverage metrics – Lead to the design of test cases that provide program coverage Defect-related metrics – Focus on bugs found rather than the tests themselves Testing effectiveness – Provide a real-time indication of the effectiveness of tests In-process metrics – Process related metrics that can be determined as testing is conducted

11. Developed by Reneta Barneva, SUNY Fredonia Metrics for Maintenance How to measure the product after it has been released to the public T DCAT M FFFM SMI )( ++- = DACT FFFM,,,