Ghulam Rasool

Design smells are structural flaws that, if not detected early, can impair software quality and increase maintenance costs. Traditional detection approaches are language-specific and rely on low-level metrics, making them ineffective for heterogeneous, multilingual systems. This paper presents Ontology-Driven Design Detector (OD3), a language-agnostic framework for detecting design smells at the design stage. OD3 reverse-engineers source code from Java, C++, C#, Ruby, and Python into a unified semantic ontology and applies rule-based reasoning to identify violations of Object-Oriented (OO) design principles. Its key contributions are: (i) detecting design smells in multilingual software projects, (ii) defining semantic representations of smells to improve interpretability, (iii) reducing incorrect detections through context-aware reasoning, (iv) introducing a catalog of cross-language design smells, and (v) validating the approach on 10 multilingual systems, achieving a precision and recall of 0.91 and 0.92, respectively. These results support scalable, early-stage detection of design flaws across heterogeneous software systems while maintaining high semantic accuracy. The proposed framework is designed to operate efficiently within High-Performance Computing (HPC) environments, where parallel ontology reasoning and distributed semantic processing facilitate the analysis of large multilingual software repositories. Consequently, the framework contributes to Sustainable Development Goal (SDG) 9 by supporting intelligent, scalable software engineering infrastructure.

Software evolution and bad smells have been widely studied over past two decades, occurring at architecture, design, implementation, and requirement levels. Design smells, which result from poor software design or violations of best programming practices, often seep into the implementation stage, causing hindrances for writing cleaner and maintainable code. Detecting design smells at design stage significantly reduces their proliferation to implementation stage. This paper introduces an innovative Ontology based Detection of Design Smell (ODDS) approach for detecting software design smells at the design level. We analyzed 4,820 classes and 27,563 methods across various Java projects, proposing new design-level metrics and axioms tailored for identifying 10 design smells. Our approach also identified that some design smells serve as precursors to the occurrence of other design smells. Unlike existing approaches that rely on source code metrics for detecting architectural and design smells, our method emphasizes using metrics, corresponding to the specific level of granularity—architectural, design, and code. Our methodology facilitates research community by early detection during the design phase, ensures design consistency by supporting automated reasoning, and reducing propagation of design smells in to implementation phase. This highlights the importance of clear and understandable designs to facilitate refactoring and minimize design smells. The efficacy of our approach is validated through its application to open-source software projects, giving precision and recall of 0.92, 0.91 and demonstrating significant potential in enhancing the detection and management of design smells.

As technology advances and new features emerge, the demand for Android applications continues to grow, leading to rapid release schedules. These accelerated development timelines often push developers to make rushed changes, often resulting in suboptimal design practices, commonly known as code smells. These issues can degrade application quality, drive up maintenance costs, lead to unexpected behaviors, and complicate evolution and re-engineering efforts. While substantial research has focused on identifying Android-specific and object-oriented code smells, comparatively less attention has been devoted to their systematic refactoring and evaluation. This study introduces a web-based technique, validated through a tool specifically developed to detect 20 Android-specific code smells and automatically refactor 10 of them. Our approach surpasses traditional desktop and plugin solutions by providing easy accessibility, cross-platform compatibility, and eliminating setup requirements. When applied to six open-source and two industrial Android applications and evaluated against the ISO/IEC 25010 quality standard, our tool demonstrated considerable improvements: reducing CPU utilization by 15.39%, lowering memory consumption by 12.85%, and enhancing battery efficiency by up to 5.78%. The tool’s accuracy, validated through precision, recall, and F-measure metrics, achieved averages of 91.81% precision, 97.77% recall, and a 94.67% F-measure. This study enhances the Android application development lifecycle by offering developers a feasible solution for optimizing CPU efficiency, reducing memory use, and minimizing battery consumption.

Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package during the coding phase, and thereby avoid external failure costs. The main goal of the study is to perform an empirical study on early defect prediction at the beginning of a project at the product level of granularity for using it as input in planning quality activities of the project. Hence, both internal and external failure costs could be avoided as much as possible through proper planning of quality. We first made a systematic mapping study (SMS) on secondary studies (literature reviews) on defect prediction to identify the most used datasets, the project attributes and metrics utilized as estimators, and the supervised learning methods employed for training the data. Then, we made an empirical study on defect density prediction using cross-project data. We collected 760 project data from the International Software Benchmarking (ISBSG) dataset version 11, which reported both defects and functional size attributes. We trained the prediction models using: i) the complete set of project attributes, ii) the individual attributes, and iii) multiple subsets of attributes. We employed classification and regression approaches of machine learning. The machine learning models are trained using original values of the dataset, and z-score and logged transformations of original values to explore the effects of data normalization on prediction. Most machine learning models trained on the z-score transformation of the dataset performed best for classifying defects. The Multilayer-Perceptron (Neural Network) model trained on the z-score transformation of complete dataset predicted defects with the highest F1-score of 0.89 using binary classification. The logged transformation and feature selection methods improved the results for multivariable regression. The multivariable regression predicted defects with the highest Root Mean Squared Error (RMSE) and R ² (r-squared) values of 0.4 and 0.9, respectively, with a subset of 11 features using logged transformation. The results of classification and regression approaches indicate that defects can be predicted with reasonable accuracy at the software product level using cross-project data.

Web Services (WSs) are gaining worldwide popularity due to reliable and fast intercommunication services for the development of web and mobile applications. WSs are provided to client application developers through web Application Programming Interfaces (APIs), such as YouTube API, Twitter API, Facebook API, etc. Due to the popularity of WSs, the developers frequently discuss various WSs-based application’ issues on online forums, such as Stack Overflow (SO). The comprehension of such conversations on SO can give insight into the frequency, difficulty, and popularity of different WSs-related problems of developers. We downloaded 12,746 posts from SO relevant to WSs-related issues for this article. We used the topic modeling technique (LDA) to extract various topics from the SO dataset. The topics are labeled and organized into categories and sub-categories according to relationships among them. The difficulty and popularity of each topic have been analyzed. Our investigation yield several findings. First, developers focus on six topics related to WSs on SO: Client APIs development, Data Processing,Web services Authorization, Framework Support,Web APIs, and Mobile Applications. Secondly, the advantages and disadvantages of web applications topic (Fused_Popularity=0.39), from the Clients APIs development category have the highest prevalence, followed by Database (DB) and Data Processing in Applications topic (Fused_Popularity=0.38) from the Data Processing category. Third, most WSs-related topics in all categories are evolving promptly on SO, i.e., new questions are added daily about WSs development, deployment, and authorization. Fourth, the questions of type “how” are primarily asked in Framework support, Client APIs development, and Web APIs categories. Although, many questions in other categories are of the kind “What”. It is also observed that WSs developers not only used SO to ask How and What types of questions but they also used SO to ask information-seeking questions (i.e., in Data processing and Client APIs development categories). Fifth, the topics relevant to Web APIs (Fused_Popularity=10.8) and Client API Development ((Fused_Popularity=9.35) categories of WSs are very popular on SO. Sixth, the questions relevant to the Web APIs (Fused_Difficulty =3) & Client APIs development (Fused_Difficulty=2.25) categories are more difficult than the other four categories. The results of our research may be helpful for the following WSs stakeholders: WSs Client application developers, WSs Educators, and WSs researchers. The WSs Educators and investigators can get more knowledge of new methods and discover novel techniques to make challenging WSs topics easy to understand. WSs framework developers can utilize our extracted WSs topics and categories to know the preferences of WSs developers that may support them in upgrading existing frameworks or developing new ones.

Software design patterns have a proven impact on the quality of software applications and the development process of an application. The success of design patterns in the software industry has attracted mobile game developers and researchers to apply patterns in the context of mobile games. Researchers have already proposed different frameworks and design patterns, but they are not truly beneficial for game developers. The high-level taxonomies can be adjuvant while proposing useful design patterns. The existing taxonomies for mobile games do not consider different parts of a game that outline top-level structure. In this paper, we propose a new taxonomy that emphasizes the top-level structure for identifying new design patterns for mobile games. We propose five novel generic design patterns that might be applied to the development of mobile games and other software applications. The presented design patterns are, in a true sense, programming patterns that outline top-level generic classes and interfaces, and that could be the basis for the development of new games. We developed four demo games by using these patterns for the realization of taxonomy and design patterns.

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. Antipatternspoor design practiceshinder this maintenance and evolution. Although the literature defines many antipatterns and proposes approaches for their (automatic) detection, their correction did 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 an engineering-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.

Humans are blessed with the intelligence to create links, develop semantic metaphors and models for reasoning; construct rules for decision making; and to form bounded loops for interaction, socialization and knowledge sharing. But machines are inadequate with these extraordinary abilities rather, numerous algorithms and mathematical models can be used to connect physical resources with cyberspaces to control objects and, develop cognitive learning for optimal decision making. Connected users and devices in closed virtual and physical proximity give direction towards the plethora of real-world applications for physical, social and, cyber computing. Because of the increase in social media networking and 5G communication links offer real-time crowdsourcing and sensing as a complementary base for information. Proceeding this idea, in this study we have proposed Cyber-Physical and Social Networks (CPSN) for two fundamental operations in IoV (Internet of Vehicles) as CPSN-IoV; (1) to define conceptual architecture of CPSN-IoV for data-oriented network for smart infrastructure and, (2) to create the significant virtual space where the instances of smart vehicles, devices, and things will have meaningful links with the real world objects where, CPSN-IoV will evolve, emerge, compete, and collaborate with all connected objects to strengthen the decision making process. To investigate the potential impact of our proposed study, we have simulated the taxicab trajectory data of the urban city of Portugal in OMNeT++ for the in-depth understanding of road topology, connected vehicles and things, and their traffic trends; and users’ social media streams in respective edge for efficient route planning. The results of simulation demonstrate that our proposed framework has the ability to achieve human-machine intellectual association for managing the smart environment.

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.