ABSTRACT Objective Antipatterns (APs) represent potential issues in software systems stemming from poor design choices, coding practices, and undisciplined development. This systematic literature review analyzes 97 primary studies (PSs) from 2005 to 2024, exploring the impact of APs on Object‐Oriented (OO), Service‐Oriented (SO), and Mobile‐Oriented (MO) systems across various quality attributes. Methods PSs are classified by techniques, datasets, evaluation measures, and tool support. Result Findings highlight the association of APs with increased maintenance costs (27.8%), fault‐proneness (26.8%), change‐proneness (12.3%), and evolution challenges (25.7%). Most studies employ descriptive statistics, regression analysis, and Pearson correlation, with limited datasets and tool support for SO and MO systems compared to OO systems. Intermediate source code representations and program comprehension strategies are commonly used for analysis. Conclusion These findings emphasize the need for further research on the impact of APs, particularly in MO systems, and their negative effects on software quality attributes.

ABSTRACT Introduction Modern software developers strive to develop applications that are robust, easy to maintain, and possess high levels of quality. However, code smells can hinder this goal as they are visible signs of underlying issues. Numerous techniques and tools have been proposed for detecting code smells in various contexts and programming languages. Despite this, research on Android‐specific code smells and their impact on external quality attributes is still in its early stages. Objective This study aims to provide a comprehensive summary of state‐of‐the‐art techniques, tools, and approaches used for detecting and refactoring code smells in Android applications. Methodology A systematic literature review was conducted between November 2007 and December 2023, adhering to standard guidelines. In total, 79 primary studies were identified, analyzed, and synthesized. Results A total of 237 code smells were identified using 51 techniques and tools, based on seven distinct approaches. Efficiency was found to be the most affected external quality attribute. The code smell Durable Wakelock was the most studied. Challenges Despite the extensive research, the software engineering community faces numerous challenges. These include a lack of in‐depth investigation into Android‐specific code smells, a limited number of studied quality attributes, insufficient involvement of industry experts in the research process, scarcity of Android‐oriented metrics, and dependence on industry‐exclusive datasets. Future Directions This review suggests potential research directions that are valuable for researchers and practitioners seeking to gain insights into current open research problems in this domain.

Many companies, e.g., Facebook and YouTube, use the REST architecture and provide REST APIs to their clients. Like any other software systems, REST APIs need maintenance and must evolve to improve and stay relevant. Antipatterns—poor design practices—hinder this maintenance and evolution. Although the literature defines many antipatterns and proposes approaches for their (automatic) detection, theircorrectiondid not receive much attention. Therefore,we apply a mixed-method approach to study REST APIs and REST antipatterns with the objectives to recommend corrections or, when possible, actually correct the REST antipatterns.Qualitatively, via case studies, we analyse the evolution of 11 REST APIs, including Facebook, Twitter, and YouTube, over six years. We detect occurrences of eight REST antipatterns in the years 2014, 2017, and 2020 in 17 versions of 11 REST APIs. Thus, we show that (1) REST APIs and antipatterns evolve over time and (2) developers seem to remove antipatterns.Qualitatively via a discourse analysis, we analyse developers’ forums and report that developers are concerned with the occurrences of REST antipatterns and discuss corrections to these antipatterns. Following these qualitative studies, using anengineering-research approach, we propose the following novel and unique contributions: (1) we describe and compare the corrections of eight REST antipatterns from the academic literature and from developers’ forums; (2) we devise and describe algorithms to recommend corrections to some of these antipatterns; (3) we present algorithms and a tool to correct some of these antipatterns by intercepting and modifying responses from REST APIs; and, (4) we validate the recommendations and the corrections manually and via a survey answered by 24 REST developers.Thus, we propose to REST API developers and researchers the first, grounded approach to correct REST antipatterns.

Summary This systematic literature review paper investigates the key techniques employed to identify smells in different paradigms of software engineering from object‐oriented (OO) to service‐oriented (SO). In this review, we want to identify commonalities and differences in the identification of smells in OO and SO systems. Our research method relies on an automatic search from the relevant digital libraries to find the studies published since January 2000 on smells until December 2017. We have conducted a pilot and author‐based search that allows us to select the 78 most relevant studies after applying inclusion and exclusion criteria. We evaluated the studies based on the smell detection techniques and the evolution of different methodologies in OO and SO. Among the 78 relevant studies selected, we have identified six different studies in which linguistic source code analysis received less attention from the researchers as compared to the static source code analysis. Smells like the yo‐yo problem, unnamed coupling, intensive coupling, and interface bloat received considerably less attention in the literature. We also identified a catalog of 30 smells infrequently reported for SO systems and that require further attention. Moreover, a suite of 20 smells reported for SO systems can also be detected using static source code metrics in OO. Finally, our review highlighted three major research trends that are further subdivided into 20 research patterns initiating the detection of smells toward their correction.

Over the past 15 years, researchers presented numerous techniques and tools for mining code smells. It is imperative to classify, compare, and evaluate existing techniques and tools used for the detection of code smells because of their varying features and outcomes. This paper presents an up-to-date review on the state-of-the-art techniques and tools used for mining code smells from the source code of different software applications. We classify selected code smell detection techniques and tools based on their detection methods and analyze the results of the selected techniques. We present our observations and recommendations after our critical analysis of existing code smell techniques and tools. Our recommendations may be used by existing and new tool developers working in the field of code smell detection. The scope of this review is limited to research publications in the area of code smells that focus on detection of code smells as compared with previous reviews that cover all aspects of code smells. Copyright © 2015 John Wiley & Sons, Ltd.

Accurately recovered design patterns support development related tasks like program comprehension and reengineering. Researchers proposed a variety of recognition approaches already. Though, much progress was made, there is still a lack of accuracy and flexibility in recognition. A major problem is the large variety of variants for implementing the same pattern. Furthermore, the integration of multiple search techniques is required to provide more accurate and effective pattern detection. In this paper, we propose variable pattern definitions composed of reusable feature types. Each feature type is assigned to one of multiple search techniques that is best fitting for its detection. A prototype implementation was applied to three open source applications. For each system a baseline was determined and used for comparison with the results of previous techniques. We reached very good results with an improved pattern catalog, but also demonstrated the necessity for customizations on new inspected systems. These results demonstrate the importance of customizable pattern definitions and multiple search techniques in order to overcome accuracy and flexibility issues of previous approaches.