On the adequacy of static analysis warnings with respect to code smell prediction

Abstract

Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine learning techniques. However, machine learning-based code smell detectors still suffer from low accuracy: one of the causes is the lack of adequate features to feed machine learners. In this paper, we face this issue by investigating the role of static analysis warnings generated by three state-of-the-art tools to be used as features of machine learning models for the detection of seven code smell types. We conduct a three-step study in which we (1) verify the relation between static analysis warnings and code smells and the potential predictive power of these warnings; (2) build code smell prediction models exploiting and combining the most relevant features coming from the first analysis; (3) compare and combine the performance of the best code smell prediction model with the one achieved by a state of the art approach. The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction.

Detecting Security Bad Smells of Software by using Data Mining

“…Code smells indicate that basic implementation principles were not followed in source code [1]. The code smell signifies the presence of a wide range of quality issues in the software.…”

“…Implementation, design, and architecture are the Detecting Security Bad Smells of Software by using Data Mining main subcategories that fall under the code smell in Java [2]. Some developers apply sub-optimal implementation decisions, negatively affecting the overall maintainability of software [1]. Many studies have shown how code smells are introduced and evolved and their influence on program comprehension [3].…”

Code smell prioritization in object‐oriented software systems: A systematic literature review

“…To maintain the properties of object-oriented systems (encapsulation, data abstraction, and modularity) and quality, there is a need to detect code smells in advance. 60,61 The large numbers of code smell detected by existing tools and approaches make it troublesome for a software developer to choose the order in which the detected code smells should be refactored. The developers do not have sufficient time to refactor every code smells.…”

Resolving Security Issues via Quality-Oriented Refactoring: A User Study

“…The popularity of Static analysis tools adoption is increasing over the years creating a humus layer for the research. In this section, we report the relevant work on static analysis tools focusing on their usage [44], [45], [12], [18], rules and the detected problems [46].…”

“…Examples of such studies include comparisons of tools in terms of capability [14], detection agreement, and precision [15]. Other studies compared specific aspects detected by the tools, such as security [16] or concurrency defects [17], [18].…”