Similar Characteristics of Internal Software Quality Attributes for Object-Oriented Open-Source Software Projects
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This document discusses a study that used cluster analysis to identify structural similarities between different software domains based on object-oriented metrics. The study analyzed over 150 open-source Java projects across different domains. The results showed that some domains tended to have similar characteristics related to abstraction, stability, complexity, and specialization. For example, security and utility software tended to have few abstract classes and descendants, while games and system administration software had more stable classes. The findings provide insights into structural aspects of object-oriented development across domains.
![INTRODUCTION (CONT.)
How to predict internal software quality?
Measuring their internal attributes (source code quality or its
complexity [ISO/IEC 25010 2011])
Why OO measures (metrics)?
Expectation: OO code should have high quality and
maintainability (organized in classes)
Why measure OO projects?
Only empirical studies on the structure of real systems can
provide tangible answers about the project’s quality
4](https://image.slidesharecdn.com/sbqs2014-mariana-140809140546-phpapp02/85/Similar-Characteristics-of-Internal-Software-Quality-Attributes-for-Object-Oriented-Open-Source-Software-Projects-4-320.jpg)
![RELATED WORK
[Romano; Pinzger 2011]: specific metrics for Java interfaces has strong
correlation with changes in abstract and concrete classes of OO projects
[Malviya; Yadav 2012]: clustering to identify OO sustainable systems
[Jehad Al Dallal 2013]: internal qualities attributes (size, cohesion and
coupling) x external quality attribute (maintainability in classes)
[Souza; Maia 2013]: reference values for a set of coupling metrics,
considering software domains
Our study:
Proposes a model that explains the similarity among domains in OO
internal software quality
It provides a more immediate view of the trends and characteristics
of internal Java software quality
25](https://image.slidesharecdn.com/sbqs2014-mariana-140809140546-phpapp02/85/Similar-Characteristics-of-Internal-Software-Quality-Attributes-for-Object-Oriented-Open-Source-Software-Projects-25-320.jpg)
Similar Characteristics of Internal Software Quality Attributes for Object-Oriented Open-Source Software Projects
- 1. Similar Characteristics of Internal Software Quality Attributes for Object-Oriented Open-Source Software Projects PqES- Grupo de pesquisa em Engenharia de Software http://pesquisa.dcc.ufla.br/pqes/index.php/home-english Universidade Federal de Lavras – UFLA – Brasil Mariana de Azevedo Santos Rodrigo Amador Heitor CostaPaulo Henrique S. Bermejo [email protected] [email protected] [email protected] [email protected]
- 2. AGENDA Introduction Objective Background Software domains Cluster analysis Methodology Results Related work Conclusion Threats of validity Future work 2
- 3. INTRODUCTION Software quality assurance: vital component for software development Organization concerns: low cost and high quality products Quality assurance are necessary, but costly: expends more 50% of the budget of projects What to do? Find efficient and cheap methods to get information about quality on software projects 3
- 4. INTRODUCTION (CONT.) How to predict internal software quality? Measuring their internal attributes (source code quality or its complexity [ISO/IEC 25010 2011]) Why OO measures (metrics)? Expectation: OO code should have high quality and maintainability (organized in classes) Why measure OO projects? Only empirical studies on the structure of real systems can provide tangible answers about the project’s quality 4
- 5. OBJECTIVE Identify similar characteristics among project structures, considering their different domains through software metrics RQ: Do the software domains have structural similarities with each other in aspects such as modularity, abstraction, stability, complexity, and specialization? Assumption: software components with similar attributes will have similar quality characteristics 5
- 6. SOFTWARE DOMAINS Software projects can be classified into different categories, related to different application domains Content: input/output, determining the nature of an application or domain Generic domains: as software complexity grows, specific domain characteristics become unclear Unclear: means that domains can have similar or dissimilar characteristics! 6
- 8. SOFTWARE DOMAINS (CONT.) 8 Science & Engineering Development System Administration Audio & Video Home & Education Security & Utilities GraphicsCommunication Business & EnterpriseAll Sourceforge domains that have web-based software System software Business software Eng. and scientific software Web-based software AI software Personal Computer Software Games Pressman’s approach
- 9. CLUSTER ANALYSIS Find similar groups of objects among themselves and different from other groups of elements Phases: 1. Selection of entities: 150 software 2. Selection of grouping attributes: metrics 3. Selection of clustering algorithm: K-means, Expectation- Maximization, hierarchical clustering 4. Data interpretation: final classification 9
- 10. CLUSTER ANALYSIS Find similar groups of objects among themselves and different from other groups of elements Phases: 1. Selection of entities: 150 software 2. Selection of grouping attributes: metrics 3. Selection of clustering algorithm: K-means, Expectation- Maximization, hierarchical clustering 4. Data interpretation: final classification 10 Macro: KM-Euclidian distance Micro: KM-Manhattan
- 11. METHODOLOGY Sample characterization: LOC: 12,178,587 Number of classes: 69,334 Repositories: Github and Sourceforge 11
- 12. METHODOLOGY (CONT.) Tools for metrics extraction: Eclipse plugins Tool for data analysis: Weka Parameters: metrics Data analysis: Hyphoteses (H0 and H1) about software quality measures relationships 12 TOOLS MEASURES Metrics VG, WMC, NOVM, NOC, DIT, SIX, LCOM, CA, CE, RMI, RMA, NC, NOM, NOA Vizz Maintenance CBO, RFC, MPC, DAC, TCC, LOC
- 13. METHODOLOGY (CONT.) What is expected? Example: For H-Depth and descendants: H0: Software domains that have classes located deeper in the inheritance hierarchy (less abstract) != software domains that have less deep classes in the inheritance hierarchy (more abstract) H1: Metrics selected are not capable of identifying characteristics among domains in relation to inheritance and abstraction. This means that the behavior on inheritance and abstraction are similar for all domains. The hypothesis is partially validated if a pair of metrics presents the expected behavior. 13
- 14. RESULTS For micro-categories and macro-categories: H1-Coupling and cohesion, H2-Complexity and inheritance, H5- Complexity and overriding were not validated H3-Depth and descendants (DIT and RMI), H4-Complexity and size (WMC and LOC) were partially validated H6-Abstraction and stability (RMA and RMI) was fully validated Macro-categories: despite having a smaller error for the same analysis for micro categories, these solutions are less heterogeneous 14
- 15. RESULTS: MICRO-CATEGORIES DIT (axis X) x RMI (axis Y): 15
- 16. RESULTS: MICRO-CATEGORIES DIT (axis X) x RMI (axis Y): 16 Cluster 0 Seem to have a more balanced relationship between abstraction and inheritance Major Domains: Science & Engineering (SE), Business & Enterprise (BE)
- 17. RESULTS: MICRO-CATEGORIES DIT (axis X) x RMI (axis Y): 17 Cluster 1 Tend to have a higher average number of descendants Major Domains: Security & Utilities (SU), Graphics (GPH)
- 18. RESULTS: MICRO-CATEGORIES DIT (axis X) x RMI (axis Y): 18 Cluster 2 Tend to have few abstract classes (more stability) and few descendants Major Domains: Development (D), Communication (C)
- 19. RESULTS: MICRO-CATEGORIES DIT (axis X) x RMI (axis Y): 19 Cluster 3 Have more stable classes than the other clusters do Major Domains: Games (G) and System Administration (SA)
- 20. RESULTS: MACRO-CATEGORIES DIT (axis X) x RMI (axis Y): 20
- 21. RESULTS: MACRO-CATEGORIES DIT (axis X) x RMI (axis Y): 21 Cluster 0 Tend to have few abstract classes (more stability) and few descendants Major Domains: PCS, SS, AIS or Audio & Video, Communication, Graphics, Development, Games...
- 22. RESULTS: MACRO-CATEGORIES DIT (axis X) x RMI (axis Y): 22 Cluster 1 Tend to have a higher average number of descendants Major Domains: PCS, SS, BS or Audio & Video, Communication, Graphics, Development, Business & Enterprise...
- 23. RESULTS: MACRO-CATEGORIES DIT (axis X) x RMI (axis Y): 23 Cluster 2 Have more stable classes than the other clusters do Major Domains: PCS, SS, AIS, ESS or Audio & Video, Communication, Graphics, Development, Games, Science & Engineering...
- 24. RESULTS: MACRO-CATEGORIES DIT (axis X) x RMI (axis Y): 24 Cluster 3 Seem to have a more balanced relationship between abstraction and inheritance Major Domains: PCS, BS, AIS, ESS or Audio & Video, Communication, Graphics, Business & Enterprise, Games, Science & Engineering...
- 25. RELATED WORK [Romano; Pinzger 2011]: specific metrics for Java interfaces has strong correlation with changes in abstract and concrete classes of OO projects [Malviya; Yadav 2012]: clustering to identify OO sustainable systems [Jehad Al Dallal 2013]: internal qualities attributes (size, cohesion and coupling) x external quality attribute (maintainability in classes) [Souza; Maia 2013]: reference values for a set of coupling metrics, considering software domains Our study: Proposes a model that explains the similarity among domains in OO internal software quality It provides a more immediate view of the trends and characteristics of internal Java software quality 25
- 26. THREATS TO VALIDITY Construct validity: other measures could be relevant and it is possible that the technique of clustering is not sufficient to completely validate and detect characteristics inherent in software projects developed in object-oriented Java Internal validity: do not provide in-depth technical details of the projects. Exemple: code inspections External validity: the study analyzes only OO software developed in Java 26
- 27. CONCLUSIONS Some specific domains tend to have similarities relating to four properties (abstraction, stability, complexity, and specialization). In general: Systems in the SU and GPH domains tend to have few descendants and few abstract classes Software in the D and C domains can have similar characteristics in inheritance and abstraction, with a higher average number of descendants Software in the SE and BE domains tend to make good use of inheritance Software in the G and SA domains can have more stable classes and are harder to maintain due to the lack of flexibility in changes 27
- 28. CONCLUSIONS (CONT.) Contribution to SE: Provide observations of structural aspects of OO development, such as specialization, stability, abstraction, and complexity Metrics such as WMC, DIT, LOC, RMI, and RMA are relevant to the characterization of Java internal software quality (similarity between domains) For software developers, the study shows that some domains (for exemple, G and SA) tend to have the same characteristics and that more efforts in these aspects are necessary so that systems can continue to be maintainable 28
- 29. FUTURE WORK We suggest: Repeating the analyses on a larger sample of software The use of other repositories of available projects The use of other metrics to obtain new results on characteristics that have not yet been explored 29
- 30. Thanks! PqES- Grupo de pesquisa em Engenharia de Software http://pesquisa.dcc.ufla.br/pqes/index.php/home-english Universidade Federal de Lavras – UFLA – Brasil Mariana de Azevedo Santos Rodrigo Amador Heitor CostaPaulo Henrique S. Bermejo [email protected] [email protected] [email protected] [email protected]
- 31. REFERENCES ISO/IEC 25010 (2011) Systems and Software Engineering - Systems and Software Quality Requirements and Evaluation - System and Software Quality Models. Dallal, J. A. (2013) Object-Oriented Class Maintainability Prediction Using Internal Quality Attributes. In: Inf. Software Technology 55, 11. pp. 2028- 2048. Malviya, A. K.; Yadav, V. K. (2012) Maintenance Activities in Object Oriented Software Systems Using K-Means Clustering Technique: A Review. In: Sixth International Conference on Software Engineering, pp. 1- 5. Romano, D.; Pinzger, M. (2011) Using Source Code Metrics to Predict Change-Prone Java Interfaces. In: International Conference on Software Maintenance, pp. 303-312. Souza, L. B. L. de; Maia, M. de A. (2013) Do Software Categories Impact Coupling Metrics? In: Working Conference on Mining Software Repositories. pp. 217-220. 31