Bad Droid! An in-depth empirical study on the occurrence and impact of Android specific code smells

Knowing the impact of bad programming practices or code smells has led researchers to conduct numerous studies in software maintenance. Most of the studies have defined code smells as bad practices that may affect the quality of the software. However, most of the existing research is heavily focused on detecting traditional code smells and less focused on mobile application specific Android code smells. Presently, there is a few papers that focus on android code smells - a catalog for Android code smells. This catalog defines 30 Android specific code smell that may impact maintainability of an app. In this research, we plan to introduce a detector tool called \textit{BadDroidDetector} for Android code smells that can detect 13 code smells from the catalog. We will also conduct an empirical study to know the distribution of 13 smell that we detect and know the severity of these smells

Combining Spreadsheet Smells for Improved Fault Prediction

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.Comment: 4 pages, 1 figure, to be published in 40th International Conference on Software Engineering: New Ideas and Emerging Results Trac

From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma