Defect prediction with bad smells in code

Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect prediction is proper selection of metrics used for dataset preparation. Objective: The purpose of this research is to verify, whether code smells metrics, collected using Microsoft CodeAnalysis tool, added to basic metric set, can improve defect prediction in industrial software development project. Results: We verified, if dataset extension by the code smells sourced metrics, change the effectiveness of the defect prediction by comparing prediction results for datasets with and without code smells-oriented metrics. In a result, we observed only small improvement of effectiveness of defect prediction when dataset extended with bad smells metrics was used: average accuracy value increased by 0.0091 and stayed within the margin of error. However, when only use of code smells based metrics were used for prediction (without basic set of metrics), such process resulted with surprisingly high accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data anomalies and problems we observed when two different metric sources were used to prepare one, consistent set of data. Conclusion: Extending the dataset by the code smells sourced metric does not significantly improve the prediction effectiveness. Achieved result did not compensate effort needed to collect additional metrics. However, we observed that defect prediction based on the code smells only is still highly effective and can be used especially where other metrics hardly be used.Comment: Chapter 10 in Software Engineering: Improving Practice through Research (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 163-176, 201

Do bad smells indicate "trouble" in code?

Part of the International Symposium on Software Testing and Analysis. Original paper can be found at : http://portal.acm.org/ Copyright ACM [Full text of this article is not available in the UHRA]In 1999 Fowler et al. identified 22 Bad Smells in code to direct the effective refactoring. These are increasingly being used by software engineers. However, the empirical basis of using Bad Smells to direct refactoring and to address 'trouble' in code is not clear. Our project aims to empirically investigate the impact of Bad Smells on software in terms of their relationship to faults

Justified test foci definition an empirical approach

Since complete testing is not possible, testers have to focus their effort on those parts of the software which they expect to have defects, the test foci. Despite the crucial importance of a systematic and justified definition of the test foci, this task is not well established in practice. Usually, testing resources are uniformly distributed among all parts of the software. A risk of this approach is that parts which contain defects are not sufficiently tested, whereas areas that do not contain defects attain too much consideration. In this thesis, a systematic approach is introduced that allows testers to make justified decisions on the test foci. For this purpose, structural as well as historical characteristics of the software’s past releases are analysed visually and statistically in order to find indicators for the software’s defects. Structural characteristics refer to the internal structure of the software. This thesis concentrates on the analysis of bad software characteristics, also known as “bad smells”. Historical characteristics considered in this thesis are the software’s change history and the software’s age. Simple and combined analyses of defect variance are introduced in order to determine indicators for defects in software. For this purpose, the defect variance analysis diagram is used to explore the relationship between the software’s characteristics and its faultiness visually. Then, statistical procedures are applied in order to determine whether the results obtained visually are statistically significant. The approach is validated in the context of open source development as well as in an industrial setting. For this purpose, seven open source programs as well as several releases of a commercial program are analysed. Thus, the thesis increases the empirical body of knowledge concerning the empirical validation of indicators for defects in software. The results show that there is a subset of bad smells that are well suited as indicators for defects in software. A good indicator in most of all analysed programs is the “God Class” bad smell. Among the historical characteristics analysed in the industrial context, the number of distinct authors as well as the number of changes performed to a file proved to be useful indicators for defects in software