Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.
Use case modeling is very popular to represent the functionality of the system to be developed, and it consists of two parts: use case diagram and use case description. Use case descriptions are written in structured natural language (NL), and the usage of NL can lead to poor descriptions such as ambiguous, inconsistent and/or incomplete descriptions, etc. Poor descriptions lead to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced use case models. This paper proposes a technique to automate detecting bad smells of use case descriptions, sym…
In large-scale embedded system development, requirement and test specifications are often expressed in natural language. In the context of developing such products, requirement review is performed in many cases manually using these specifications as a basis for quality assurance. Low-quality specifications can have expensive consequences during the requirement engineering process. Especially, if feedback loops during requirement engineering are long, leading to artifacts that are not easily maintainable, are hard to understand, and are inefficient to port to other system variants. We use the i…
Anti-patterns and code-smells are signs in the source code which are not defects (does not prevent the program from functioning and does not cause compile errors) and are rather indicators of deeper and bigger problems. Exception handling is a programming construct de- signed to handle the occurrence of anomalous or exceptional conditions (that changes the normal flow of program execution). In this paper, we present an Eclipse plug-in (called as Parichayana) for detecting exception handling anti-patterns and code smells in Java programs. Parichayana is capable of automatically detecting severa…
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…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Bucerius Law School（汉堡博锐思法学院）；Stanford Law School（斯坦福法学院）；Ruprecht-Karls-Universität Heidelberg（海德堡大学）；Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Illinois Tech – Chicago Kent College of Law（伊利诺伊理工大学芝加哥肯特法学院）
Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples - namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession -, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to …
TU Delft（代尔夫特理工大学）；AI for Fintech Research, ING（荷兰国际集团金融科技人工智能研究部）；ML Engineering Chapter, ING（荷兰国际集团机器学习工程部）
Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel con…
Code smells indicate software design problems that harm software quality. Data-intensive systems that frequently access databases often suffer from SQL code smells besides the traditional smells. While there have been extensive studies on traditional code smells, recently, there has been a growing interest in SQL code smells. In this paper, we conduct an empirical study to investigate the prevalence and evolution of SQL code smells in open-source, data-intensive systems. We collected 150 projects and examined both traditional and SQL code smells in these projects. Our investigation delivers se…
Unit testing is an essential component of the software development life-cycle. A developer could easily and quickly catch and fix software faults introduced in the source code by creating and running unit tests. Despite their importance, unit tests are subject to bad design or implementation decisions, the so-called test smells. These might decrease software systems quality from various aspects, making it harder to understand, more complex to maintain, and more prone to errors and bugs. Many studies discuss the likely effects of test smells on test code. However, there is a lack of studies tha…
Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in th…
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is prominent to experienced software developers with acceptable coding practices. Recent studies have often observed that codes having code smells are often prone to a higher probability of change in the software development cycle. In this paper, we developed code smell prediction models with the help of features extracted from source code to predict eight types of…
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）
Birmingham City University（伯明翰城市大学）；University of Bristol（布里斯托大学）
In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political-related tweets is a challenging task due to the lack of explicit sent…
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 …
Human-AI Collaboration in Requirements Engineering: Evidence of the Negative Effect of LLMs on Requirements Inspection
CNR–ISTI, Pisa, Italy（意大利比萨国家研究委员会信息与通信技术研究所）；Chalmers University of Technology, Gothenburg, Sweden（瑞典哥德堡查尔姆斯理工大学）；Monash University, Melbourne, Australia（澳大利亚墨尔本蒙纳士大学）；University of Florence, Florence, Italy（意大利佛罗伦萨大学）；University of Duisburg-Essen（德国杜伊斯堡-埃森大学）；Trinity College Dublin, Dublin, Ireland（爱尔兰都柏林三一学院）
Cloud applications are more and more microservice-oriented, but a concrete charting of the microservices architecture landscape -- namely, the space of technical options available for microservice software architects in their decision-making -- is still very much lacking, thereby limiting the ability of software architects to properly evaluate their architectural decisions with sound experiential devices and/or practical design principles. On the one hand, Microservices are fine-grained, loosely coupled services that communicate through lightweight protocols. On the other hand, each microservi…
Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model,…
Context. The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions (e.g., e-sports or speed-running) or for making business by entertaining others (e.g., streamers). The latter daily produce a large amount of gameplay videos in which they also comment live what they experience. Since no software and, thus, no video game is perfect, streamers may encounter several problems (such as bugs, glitches, or performance issues). However, it is unlikely that they explicitly report such issues to …
Context: Profiling developers is challenging since many factors, such as their skills, experience, development environment and behaviors, may influence a detailed analysis and the delivery of coherent interpretations. Objective: We aim at profiling software developers by mining their software development process. To do so, we performed a controlled experiment where, in the realm of a Python programming contest, a group of developers had the same well-defined set of requirements specifications and a well-defined sprint schedule. Events were collected from the PyCharm IDE, and from the Mooshak a…
