Evaluating the effectiveness of object-oriented metrics for bug prediction

In our experiments we examined the general relationship between object-oriented metrics and the fault-proneness of classes. We analyzed a large open-source program called Mozilla, calculated 58 object-oriented metrics for Mozilla at the class level \cite FSG04, collected the reported and corrected bugs from the bug tracking system of Mozilla and associated them with the classes. We applied logistic regression to examine which metrics could be used to predict the fault proneness of the classes. We found that 17 of the 58 object-oriented metrics were useful predictors, but to a different extent. The CBO (Coupling Between Object classes) metric was the best, but it was only slightly better than NOI (Number of Outgoing Invocations) and RFC (Response Set for a Class), which proved useful as well. We also examined the metrics in terms of their categories and we found that coupling metrics were the best predictors for finding bugs, but the complexity and size metrics also gave good results. On the other hand, in tests all the inheritance-related metrics were statistically insignificant

The software developers’ view on product metrics — A survey-based experiment

Object-oriented metrics are becoming evermore popular and they are used in many different areas of software development. Many researchers have showed in practice that object-oriented metrics can be efficiently used for quality assurance. For example, a lot of experimental results confirm that some of the object-oriented metrics (like coupling, size, and complexity) are able to predict the fault-proneness of classes. Quality assurance experts usually accept that actively applying metrics can help their work. On the other hand, developers tend not to use metrics because they do not know about them, or if they do know about them, they do not really know how to use them. Hence we devised a Survey to ask developers with different levels of experience about the use of metrics. Our hypothesis was that developers with different levels of experience might have significantly different views about the usefulness of metrics. In the Survey four metrics (size, complexity, coupling, and code duplication) were examined. The Survey asked questions about the participants’ experience and skills, then it asked questions about how the participants would probably use these metrics for software testing or program comprehension, and at the end the relative importance of the metrics was assessed. The main result of the Survey is a list which contains those cases where the views about the metrics from developers having different experience significantly differ. We think that getting to know the developers’ views better can help us to create better quality models based on object-oriented metrics

Hatékony rendszer-szintű hatásanalízis módszerek és alkalmazásuk a szoftverfejlesztés folyamatában = Efficient whole-system impact analysis methods with applications in software development

Szoftver hatásanalízis során a rendszer megváltoztatásának következményeit becsüljük, melynek fontos alkalmazásai vannak például a változtatás-propagálás, költségbecslés, szoftverminőség és tesztelés területén. A kutatás során olyan hatásanalízis módszereket dolgoztunk ki, melyek hatékonyan és sikeresen alkalmazhatók nagyméretű és heterogén architektúrájú, valós alkalmazások esetében is. A korábban rendelkezésre álló módszerek csak korlátozott méretben és környezetekben voltak képesek eredményt szolgáltatni. A meglévő statikus és dinamikus programszeletelés és függőség elemzési algoritmusok továbbfejlesztése mellett számos kapcsolódó területen értünk el eredményeket úgy, mint függőségek metrikákkal történő vizsgálata, fogalmi csatolás kutatása, minőségi modellek, hiba- és produktivitás előrejelzés. Ezen területeknek a módszerek gyakorlatban történő alkalmazásában van jelentősége. Speciális technológiákra koncentrálva újszerű eredmények születtek, például adatbázis rendszerek vagy alacsony szintű nyelvek esetében. A hatásanalízis módszerek alkalmazásai terén kidolgoztunk újszerű módszereket a tesztelés optimalizálása, teszt lefedettség mérés, -priorizálás és változás propagálás területeken. A kidolgozott módszerek alapját képezték további projekteknek, melyek során szoftvertermékeket is kiegészítettek módszereink alapján. | During software change impact analysis, we assess the consequences of changes made to a software system, which has important applications in, for instance, change propagation, cost estimation, software quality and testing. We developed impact analysis methods that can be effectively and efficiently used for large and heterogeneous real life applications as well. Previously available methods could provide results only in limited environments and for systems of limited size. Apart from the enhancements developed for the existing static and dynamic slicing and dependence analysis algorithms, we achieved results in different related areas such as investigation of dependences based on metrics, conceptual coupling, quality models and prediction of defects and productivity. These areas mostly support the application of the methods in practice. We have contributions in the fields of different special technologies, for instance, dependences in database systems or analysis of low level languages. Regarding the applications of impact analysis, we developed novel methods for test optimization, test coverage measurement and prioritization, and change propagation. The developed methods provided basis for further projects, also for extension of certain software products

Optimized angiotensin-converting enzyme activity assay for the accurate diagnosis of sarcoidosis

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