Irum Inayat

The structural features of a code section that may indicate a more serious issue with the design of a system or code are known as code smells. Design patterns, on the other hand, are meant to describe the best reusable solution for creating object‐oriented software systems. Even though design patterns and code smells are very different, they may co‐occur. In fact, there may be a significant connection among the two, which requires further research. This study aims to (i) identify design patterns and code smells in web gaming code, (ii) investigate the co‐occurrence of the two, and (iii) analyze the effects of these co‐occurrences on internal quality aspects of code. An experiment is carried out on JavaScript (JS) web games utilizing machine learning classifiers to investigate the influence of co‐occurrence on potential code smells and design patterns to evaluate games from a quality perspective. Moreover, statistical testing is performed to identify the impact of co‐occurrences of code smells and design patterns on internal quality attributes. After examining the data, we determined that random forest is the most effective classifier, achieving an accuracy of 99.126% and 98.99% for both experimental situations, respectively. Moreover, on applying the Wilcoxon signed rank test, we found that co‐occurrence has no impact on the coupling and complexity of web games codes, whereas there is a significant impact of co‐occurrence on cohesion, size, and inheritance. Our results may guide developers in writing efficient games code to add to this swiftly growing market.

Mobile app development necessitates extracting domain-specific, essential, and innovative features that align with user needs and market trends. Determining which features provide a competitive advantage is a complex task, often managed manually by product managers. This study addresses the challenge of automating feature mining and recommendation by identifying similar apps based on user-provided descriptions. The proposed approach integrates Named Entity Recognition (NER) for feature extraction from mined Google Play app data with BERT (Bidirectional Encoder Representations from Transformers) and Topic Modeling to find comparable apps. Our top-performing model, which uses Non-negative Matrix Factorization (NMF) for Topic Modeling with Sentence-BERT (SBERT) embeddings, achieves an F1 score of 87.38%.

Cyber‐physical systems (CPS) are applicable in emerging industries such as health, energy, autonomous vehicles, and Industrial Internet of Things (IIOT). In CPS the physical assets, that is, actuators and sensors, etc. communicate with each other over a messaging protocol. This communication process of CPS makes them vulnerable to cyber‐attacks which challenge the system safety, making security and safety two major concerns for CPS. Both safety and security risks are considered separately in literature. However, the International Society of Automation (ISA) focuses on the alignment of security and safety risks of CPS. Weak or no alignment in safety and security of a system may result in an inefficient or partially protected system which could end up in disasters. This pressed the need for an integrated safety‐security risk management process. For this purpose, we used a tetra packaging case study to (i) examine the vulnerabilities of CPS by running the risk management process, (ii) identify safety‐security requirements, and (iii) align retrieved safety‐security requirements with the relevant standards. The results show (i) safety hazards and security risks along with their severity and priority, (ii) mitigation guidelines are provided by consulting IEC 61508, and (iii) 15 safety‐security requirements are identified and are aligned with ISO9001 Packaging and labeling machine standard.

The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy.