Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt does not offer actionable advice on how to get rid of it. To get to a practical level in solving problems, more focused mechanisms are needed. Commonly used approaches for this include identifying code smells as quick indications of possible problems in the codebase and detecting the presence of AntiPatterns that refer to overt, recurring problems in design. There are known remedies for both code smells and AntiPatterns. In paper, our goal is to show how to effectively use common tools and the existing body of knowledge on code smells and AntiPatterns to detect technical debt and pay it back. We present two main results: (i) How a combination of static code analysis and manual inspection was used to detect code smells in a codebase leading to the discovery of AntiPatterns; and (ii) How AntiPatterns were used to identify, characterize, and fix problems in the software. The experiences stem from a private company and its long-lasting software product development effort.
Context: Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt that impact software teams, development processes, outcomes, and organizations. Objective: To provide an overview of community smells based on published literature, and describe future research. Method: We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explai…
University of Stuttgart（斯图加特大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Background: With the rising popularity of Artificial Intelligence (AI), there is a growing need to build large and complex AI-based systems in a cost-effective and manageable way. Like with traditional software, Technical Debt (TD) will emerge naturally over time in these systems, therefore leading to challenges and risks if not managed appropriately. The influence of data science and the stochastic nature of AI-based systems may also lead to new types of TD or antipatterns, which are not yet fully understood by researchers and practitioners. Objective: The goal of our study is to provide a cl…
Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to characterize how developers manage technical debt based on the code smells they induce and the refactorings they apply. Method: We mined a publicly-available Technical Debt dataset for Git commit information, code smells, coding violations, and refactoring activities for each developer of a selected project. Results: By combining this information, we profile develo…
Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts. Empirical results on open-source systems demonstrated how code-smells, which are just one type of TDIs, are introduced and "survive" during release cycles. However, little is known about whether the results on the survivability of code-smells hold for other types of code TDIs (i.e., bugs and vulnerabilities) and in industrial settings. Goal: Understanding the…
Code smells as symptoms of poor design and implementation choices. Many times they are the result of so called technical debt. Our study showed that the interest in code smells research is increasing. However, most of the publications are appearing in conference proceedings. Most of the research is done in G7 and other highly developed countries. Four main research themes were identified namely code smell detection, bad smell based refactoring, software development and anti patterns. The results show that code smells can also have a positive connotation, we can develop software which smells go…
Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The negative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to comprehend source code, researchers empirically showed that the presence of code smells heavily impacts the change-proneness of the affected classes. On the basis of these findings, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, ie models having as goal that of …
The learning and usage of an API is supported by official documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features difficult. Indeed, similar to code smells or code antipatterns, poorly designed API documentation can also exhibit 'smells'. Such documentation smells can be described as bad documentation styles that do not necessarily produce an incorrect documentation but nevertheless make the documentation difficult to properly understand and to use. Recent research on …
Technical Debt analysis is increasing in popularity as nowadays researchers and industry are adopting various tools for static code analysis to evaluate the quality of their code. Despite this, empirical studies on software projects are expensive because of the time needed to analyze the projects. In addition, the results are difficult to compare as studies commonly consider different projects. In this work, we propose the Technical Debt Dataset, a curated set of project measurement data from 33 Java projects from the Apache Software Foundation. In the Technical Debt Dataset, we analyzed all c…
Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies show that code smells and architectural smells often appear together in the same file. The correlation between code smells and architectural smells, however, is not clear yet; some studies on a limited set of projects have claimed that architectural smells can be derived from code smells, while other studies claim the opposite. Objective. The goal of this work is to understand …
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension, increase code complexity and fault-proneness and decrease maintainability of software systems. The aim of our study was to perform a triangulation of bibliometric and thematic analysis on code smell literature production. The search was performed on Scopus (Elsevier, Netherlands) database using the search string code smells which resulted in 442 publications. The Go-to statement was the first bad code smells identified in software engineering history in 1968. The literature production trend ha…
The ever-increasing amount, variety as well as generation and processing speed of today's data pose a variety of new challenges for developing Data-Intensive Software Systems (DISS). As with developing other kinds of software systems, developing DISS is often done under severe pressure and strict schedules. Thus, developers of DISS often have to make technical compromises to meet business concerns. This position paper proposes a conceptual model that outlines where Technical Debt (TD) can emerge and proliferate within such data-centric systems by separating a DISS into three parts (Software Sy…
Software engineering is a human activity. Despite this, human aspects are under-represented in technical debt research, perhaps because they are challenging to evaluate. This study's objective was to investigate the relationship between technical debt and affective states (feelings, emotions, and moods) from software practitioners. Forty participants (N = 40) from twelve companies took part in a mixed-methods approach, consisting of a repeated-measures (r = 5) experiment (n = 200), a survey, and semi-structured interviews. The statistical analysis shows that different design smells (strong ind…
Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers' personalities could influence community smells' var…
Code smells are seen as major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. We proposed the crowdsmelling approach based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue. This paper presents the results of a validation experiment for the crowdsmelling approac…
To reduce technical debt and make code more maintainable, it is important to be able to warn programmers about code smells. State-of-the-art code small detectors use deep learners, without much exploration of alternatives within that technology. One promising alternative for software analytics and deep learning is GHOST (from TSE'21) that relies on a combination of hyper-parameter optimization of feedforward neural networks and a novel oversampling technique to deal with class imbalance. The prior study from TSE'21 proposing this novel "fuzzy sampling" was somewhat limited in that the method w…
Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
Adelaide University（阿德莱德大学）；University of Victoria（维多利亚大学）；Huazhong University of Science and Technology（华中科技大学）；Singapore Management University（新加坡管理大学）
To effectively manage Technical Debt (TD), we need reliable means to quantify it. We conducted a Systematic Mapping Study (SMS) where we identified TD quantification approaches that focus on different aspects of TD. Some approaches base the quantification on the identification of smells, some quantify the Return on Investment (ROI) of refactoring, some compare an ideal state with the current state of a software in terms of the software quality, and some compare alternative development paths to reduce TD. It is unclear if these approaches are quantifying the same thing and if they support simil…
Software design smells are design attributes which violate the fundamental design principles. Design smells are a key cause of design debt. Although the activities of design smell identification and measurement are predominantly considered in current literature, those which identify and communicate which design smells occur more frequently in newly developing software and which ones are more dominant in established software have been studied to a limited extent. This research describes a mechanism for identifying the design smells that are more prevalent in developing and established software …
FedCSD: A Federated Learning Based Approach for Code-Smell Detection
Center for Applied Intelligent Systems Research（应用智能系统研究中心）；School of Information Technology（信息技术学院）；Halmstad University（哈尔姆斯塔德大学）；Prince Abdullah bin Ghazi Faculty of Information and Communication Technology（阿卜杜拉·本·加齐王子信息与通信技术学院）；Al-Balqa Applied University（巴尔卡应用大学）；Internet of Things and People Research Center（物联网与人研究中心）；Malmö University（马尔默大学）；Department of Computer Science（计算机科学系）；Blekinge Institute of Technology（布莱金厄理工学院）；Institute of Computer Science（计算机科学研究所）；Delta Research Centre（德尔塔研究中心）；University of Tartu（塔尔图大学）；SeSa Lab（SeSa实验室）；University of Salerno（萨勒诺大学）；Arab American University（阿拉伯美国大学）
The context of this work is specification, detection and ultimately removal of detectable harmful patterns in source code that are associated with defects in design and implementation of software. In particular, we investigate five code smells and four antipatterns previously defined in papers and books. Our inquiry is about detecting those in source code written in Python programming language, which is substantially different from all prior research, most of which concerns Java or C-like languages. Our approach was that of software engineers: we have processed existing research literature on …
With the ever-increasing use of games, game developers are expected to write efficient code supporting several qualities such as security, maintainability, and performance. However, the continuous need to update the features of games in less duration might compel the developers to use anti-patterns, code smells and quick-fix solutions that may affect the functional and non-functional requirements of the game. These bad practices may lead to technical debt, poor program comprehension, and can cause several issues during software maintenance. Here, in this paper, we introduce "Software Nuggets" …
