Software, Volume 3, Issue 3 (September 2024) – 7 articles

The integration of sign language recognition systems into digital platforms has the potential to bridge communication gaps between the deaf community and the broader population. This paper introduces an advanced Israeli Sign Language (ISL) recognition system designed to interpret dynamic motion gestures, addressing a critical need for more sophisticated and fluid communication tools. Unlike conventional systems that focus solely on static signs, our approach incorporates both deep learning and Computer Vision techniques to analyze and translate dynamic gestures captured in real-time video. We provide a comprehensive account of our preprocessing pipeline, detailing every stage from video collection to the extraction of landmarks using MediaPipe, including the mathematical equations used for preprocessing these landmarks and the final recognition process. The dataset utilized for training our model is unique in its comprehensiveness and is publicly accessible, enhancing the reproducibility and expansion of future research. The deployment of our model on a publicly accessible website allows users to engage with ISL interactively, facilitating both learning and practice. We discuss the development process, the challenges overcome, and the anticipated societal impact of our system in promoting greater inclusivity and understanding. Full article

The advent of blockchain technology has revolutionized various sectors by providing transparency, immutability, and automation. Central to this revolution are smart contracts, which facilitate trustless and automated transactions across diverse domains. However, the proliferation of smart contracts has exposed significant security vulnerabilities, necessitating advanced analysis techniques. Data dependency analysis is a critical program analysis method used to enhance the testing and security of smart contracts. This paper introduces Sligpt, an innovative methodology that integrates a large language model (LLM), specifically GPT-4o, with the static analysis tool Slither, to perform data dependency analyses on Solidity smart contracts. Our approach leverages both the advanced code comprehension capabilities of GPT-4o and the advantages of a traditional analysis tool. We empirically evaluate Sligpt using a curated dataset of Ethereum smart contracts. Sligpt achieves significant improvements in precision, recall, and overall analysis depth compared with Slither and GPT-4o, providing a robust solution for data dependency analysis. This paper also discusses the challenges encountered, such as the computational resource requirements and the inherent variability in LLM outputs, while proposing future research directions to further enhance the methodology. Sligpt represents a significant advancement in the field of static analysis on smart contracts, offering a practical framework for integrating LLMs with static analysis tools. Full article

The automotive industry is experiencing a significant shift, transitioning from traditional hardware-centric systems to more advanced software-defined architectures. This change is enabling enhanced autonomy, connectivity, safety, and improved in-vehicle experiences. Service-oriented architecture is crucial for achieving software-defined vehicles and creating new business opportunities for original equipment manufacturers. A software update approach that is rich in variability and based on a Merkle tree approach is proposed for new vehicle architecture requirements. Given the complexity of software updates in vehicles, particularly when dealing with multiple distributed electronic control units, this software-centric approach can be optimized to handle various architectures and configurations, ensuring consistency across all platforms. In this paper, our software update approach is expanded to cover the solution space of the feature-based product line engineering, and we show how to combine our approach with product line engineering in creative and unique ways to form a software-defined vehicle modular architecture. Then, we offer insights into the design of the Merkle trees utilized in our approach, emphasizing the relationship among the software modules, with a focus on their impact on software update performance. This approach streamlines the software update process and ensures that the safety as well as the security of the vehicle are continuously maintained. Full article

Software vulnerabilities pose a significant threat to computer systems because they can jeopardize the integrity of both software and hardware. The existing tools for detecting vulnerabilities are inadequate. Machine learning algorithms may struggle to interpret enormous datasets because of their limited ability to understand intricate linkages within high-dimensional data. Traditional procedures, on the other hand, take a long time and require a lot of manual labor. Furthermore, earlier deep-learning approaches failed to acquire adequate feature data. Self-attention mechanisms can process information across large distances, but they do not collect structural data. This work addresses the critical problem of inadequate vulnerability detection in software systems. We propose a novel method that combines self-attention with convolutional networks to enhance the detection of software vulnerabilities by capturing both localized, position-specific features and global, content-driven interactions. Our contribution lies in the integration of these methodologies to improve the precision and F1 score of vulnerability detection systems, achieving unprecedented results on complex Python datasets. In addition, we improve the self-attention approaches by changing the denominator to address the issue of excessive attention heads creating irrelevant disturbances. We assessed the effectiveness of this strategy using six complex Python vulnerability datasets obtained from GitHub. Our rigorous study and comparison of data with previous studies resulted in the most precise outcomes and F1 score (99%) ever attained by machine learning systems. Full article

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are characterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) has emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We prove that a Petri net and its corresponding CCA process are behavioural equivalent. It follows that CCA is at least as expressive as Petri nets, i.e., any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse the behaviours of Petri nets. Full article

In software engineering, there must be clarity in communication among interested parties to elicit the requirements aimed at software development through frameworks to achieve the behaviors expected by the software. Problem: A lack of clarity in the requirement-elicitation stage can impact subsequent stages of software development. Solution: We proposed a case study focusing on the performance efficiency characteristic expressed in the ISO/IEC/IEEE 25010 standard using Behavior-Driven Development (BDD). Method: The case study was performed with professionals who use BDD to elicit the non-functional requirements of a company that develops software. Summary of Results: The result obtained was the validation related to the elicitation of non-functional requirements aimed at the performance efficiency characteristic of the ISO/IEC/IEEE 25010 Standard using the BDD framework through a real case study in a software development company. Contributions and impact: The article’s main contribution is to demonstrate the effectiveness of using BDD to elicit non-functional requirements about the performance efficiency characteristic of the ISO/IEC/IEEE 25010 standard. Full article

The number of mobile applications (“Apps”) has grown significantly in recent years. App Stores rank/recommend Apps based on factors such as average star ratings and the number of installs. Such rankings do not focus on the internal artifacts of Apps (e.g., security vulnerabilities). If internal artifacts are ignored, users may fail to estimate the potential risks associated with installing Apps. In this research, we present a framework called E-SERS (Enhanced Security-related and Evidence-based Ranking Scheme) for comparing Android Apps that offer similar functionalities. E-SERS uses internal and external artifacts of Apps in the ranking process. E-SERS is a significant enhancement of our past evidence-based ranking framework called SERS. We have evaluated E-SERS on publicly accessible Apps from the Google Play Store and compared our rankings with prevalent ranking techniques. Our experiments demonstrate that E-SERS, leveraging its holistic approach, excels in identifying malicious Apps and consistently outperforms existing alternatives in ranking accuracy. By emphasizing comprehensive assessment, E-SERS empowers users, particularly those less experienced with technology, to make informed decisions and avoid potentially harmful Apps. This contribution addresses a critical gap in current App-ranking methodologies, enhancing the safety and security of today’s technologically dependent society. Full article