1 ECE 453 – CS 447 – SE 465 Software Testing & Quality Assurance Case Studies Instructor Paulo Alencar

2 Recommended 1. Olague, H., Etzkorn, L., Ghoston, S., Quattlebaum, S., Empirical Validation of Three Software Metric Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Process, IEEE Transactions on Software Engineering, vol. 33, no.4, pp , Fioravanti, F., Nesi, P., Estimation and Prediction Metrics for Adaptive Maintenance Effort of Object- Oriented Systems, IEEE Transactions on Software Engineering, vol. 27, no. 12, pp , 2001.

3 Recommended 3. Subramanyam, R., Krishnan, M., Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects, IEEE Transactions on Software Engineering, vol. 29, no.4, pp , Bandi, R., Vaishnavi, V., Turk, D., Predicting Maintenance Performance Using Object-Oriented Design Complexity Metrics, IEEE Transactions on Software Engineering, vol. 29, no. 1, pp.77-87, 2003.

4 Overview To assess the ability of OO metrics to identify fault- prone components in different development environments (e.g., agile process) Validation of three OO metric suites A large software system is evaluated (Mozilla Rhino project) Olague, H., Etzkorn, L, Ghoston, S., Quattlebaum, S., Empirical Validation of Three Software Metric Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Process, IEEE Transactions on Software Eng., vol. 33(6), pp , 2007.

5 Main Results Metric definitions – first suite:

6 Main Results Metric definitions – second suite:

7 Main Results Metric definitions – third suite:

8 Main Results Software examined: Mozilla Rhino – an open source implementation of JavaScript written in Java An example of the use of the agile software development in open source software Six Rhino versions were analyzed in this case study Delivery cycle time from 2 to 16 months

9 Main Results Hypotheses: Hypothesis 1: OO metrics can identify fault-prone classes in traditional and highly iterative or agile developed OO software during its initial delivery Hypothesis 2: OO metrics can identify fault-prone classes in multiple sequential releases of OO software systems developed and using highly iterative or agile software development process

10 Main Results Model validation:

11 Main Results CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error- prone classes MOOD metrics suite are not good class fault-proneness predictors The produced models can be useful in assessing quality in OO classes developed using modern highly iterative or agile software development processes