This paper introduces ALICE (Automated Logic for Identifying Contradictions in Engineering), a novel automated contradiction detection system tailored for formal requirements expressed in controlled natural language. By integrating formal logic with advanced large language models (LLMs), ALICE represents a significant leap forward in identifying and classifying contradictions within requirements documents. Our methodology, grounded on an expanded taxonomy of contradictions, employs a decision tree model addressing seven critical questions to ascertain the presence and type of contradictions. A pivotal achievement of our research is demonstrated through a comparative study, where ALICE’s performance markedly surpasses that of an LLM-only approach by detecting 60% of all contradictions. ALICE achieves a higher accuracy and recall rate, showcasing its efficacy in processing real-world, complex requirement datasets. Furthermore, the successful application of ALICE to real-world datasets validates its practical applicability and scalability. This work not only advances the automated detection of contradictions in formal requirements but also sets a precedent for the application of AI in enhancing reasoning systems within product development. We advocate for ALICE’s scalability and adaptability, presenting it as a cornerstone for future endeavors in model customization and dataset labeling, thereby contributing a substantial foundation to requirements engineering.

As bigger quantum processors with hundreds of qubits become increasingly available, the potential for quantum computing to solve problems intractable for classical computers is becoming more tangible. Designing efficient quantum algorithms and software in tandem is key to achieving quantum advantage. Quantum software engineering is challenging due to the unique counterintuitive nature of quantum logic. Moreover, with larger quantum systems, traditional programming using quantum assembly language and qubit-level reasoning is becoming infeasible. Automated Quantum Software Engineering (AQSE) can help to reduce the barrier to entry, speed up development, reduce errors, and improve the efficiency of quantum software. This article elucidates the motivation to research AQSE (why), a precise description of such a framework (what), and reflections on components that are required for implementing it (how).

With the addition of multiple blockchain platforms in the ecosystem, the Dapp owners need to migrate their smart contracts from one platform to another to remain competitive, cost-effective, and secure. A smart contract is a piece of code that contain logic and data. To migrate a smart contract, whether it's on the same blockchain platform or a different one, we need both its source code that represents the logic and data which indicate the state of the contract. The source code can be easily set up, but to complete the migration, we have to extract the current state of the contract. In this paper, we have developed an advanced state extraction technique that uses static analysis to analyze the smart contract's call graph and events, and extracts the entire storage state from the storage trie, along with the proper associations across function calls, enabling users to visualize, manage, and transform the state as desired for migration. The soundness of the extracted state was confirmed using the method of abstract interpretation. Further, the migration adapter is designed that transform extracted state into slot-value pair and migrated it to the target blockchain. Our new approach has allowed us to analyze 14% more smart contracts with the extraction of 15% more data from 67,993 contracts, and migrate some of them to the Polygon test-net.

The porting or translation of software applications from one programming language to another is a common requirement of organisations that utilise software, and the increasing number and diversity of programming languages makes this capability as relevant today as in previous decades. Several approaches have been used to address this challenge, including machine learning and the manual definition of direct language-to-language translation rules, however the accuracy of these approaches remains unsatisfactory. In this paper we describe a new approach to program translation using model-driven engineering techniques: reverse-engineering source programs into specifications in the UML and OCL formalisms, and then forward-engineering the specifications to the required target language. This approach can provide assurance of semantic preservation, and additionally has the advantage of extracting precise specifications of software from code. We provide an evaluation based on a comprehensive dataset of examples, including industrial cases, and compare our results to those of other approaches and tools. Our specific contributions are: (1) Reverse-engineering source programs to detailed semantic models of software behaviour, to enable semantically-correct translations and reduce re-testing costs; (2) Program abstraction processes defined by precise and explicit rules, which can be edited and configured by users; (3) A set of reusable OCL library components appropriate for representing program semantics, and which can also be used for OCL specification of new applications; (4) A systematic procedure for building program abstractors based on language grammars and semantics.

The recent advent of data protection laws and regulations has emerged to protect privacy and personal information of individuals. As the cases of privacy breaches and vulnerabilities are rapidly increasing, people are aware and more concerned about their privacy. These bring a significant attention to software development teams to address privacy concerns in developing software applications. As today’s software development adopts an agile, issue-driven approach, issues in an issue tracking system become a centralised pool that gathers new requirements, requests for modification and all the tasks of the software project. Hence, establishing an alignment between those issues and privacy requirements is an important step in developing privacy-aware software systems. This alignment also facilitates privacy compliance checking which may be required as an underlying part of regulations for organisations. However, manually establishing those alignments is labour intensive and time consuming. In this paper, we explore a wide range of machine learning and natural language processing techniques which can automatically classify privacy requirements in issue reports. We employ six popular techniques namely Bag-of-Words (BoW), N-gram Inverse Document Frequency (N-gram IDF), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to perform the classification on privacy-related issue reports in Google Chrome and Moodle projects. The evaluation showed that BoW, N-gram IDF, TF-IDF and Word2Vec techniques are suitable for classifying privacy requirements in those issue reports. In addition, N-gram IDF is the best performer in both projects.

The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps.