Background: The rapid adoption of Large Language Models (LLMs) in software engineering, such as in customer service chatbots, has brought substantial benefits but also significant risks. Biased, inappropriate, or harmful responses may occur when integrating LLMs as commercial off-the-shelf (COTS) components into user-facing applications. Aims: This paper aims to present emerging results from an exploratory study that compares commercial guardrail frameworks, assessing their effectiveness in filtering inappropriate content during chat interactions. Method: We empirically evaluated three guardrail frameworks LLM Guard, Llama Guard, and OpenAI Moderation - using two datasets containing toxic and offensive content. Results: The frameworks achieved high accuracy for one dataset (over 90%) but underperformed in other metrics, indicating that toxic or dangerous content could still reach users in certain scenarios, such as deployment in chatbots. Conclusions: These findings$h$ighlight$t$he$n$eed$f$or further improvements in guardrail frameworks and provide insights for researchers and practitioners to support the selection of appropriate solutions for enhancing harm mitigation in LLMbased applications.

Ensuring complete software requirements specifications (SRS) is critical to preventing costly downstream errors. We present a tool that ensembles three complementary LLMs—DeepSeek Chat, GPT-4o Mini, and Claude Sonnet 4—to detect and suggest remedies for missing requirements. The tool generates a structured domain model and applies parallel external and internal completeness checks through tailored prompts. Users can select LLMs and aggregation methods (majority, weighted, or meta-fusion). Unlike prior single-model approaches, we systematically evaluate aggregation strategies across four diverse SRS domains. In experiments with seeded omissions, single models achieved only 0–52% recall, whereas our ensemble consistently exceeded 75%—reaching up to 100%—with 95–100% plausibility. These results demonstrate the feasibility of multi-LLM ensembles as practical aids—complementing rather than replacing human analysts—and supporting interactive refinement workflows.

Security and reliability in Blockchain software systems is a major challenge in Blockchain Oriented Software Engineering. One of the most critical components to address at the architectural level is the consensus protocol, as it serves as the mechanism for accepting valid transactions and incorporating them into the ledger history. Given that this process is executed by specific blockchain nodes, it is crucial to consider them as a key point of focus for ensuring the integrity of the entire blockchain history. This paper addresses the major challenge of security and reliability in Blockchain software systems by proposing a new protocol for Permissioned Concurrent Proof of Authority (CPoA). This protocol involves selecting a group of nodes as authority nodes, responsible for validating new identities, blocks, and transactions. The protocol is integrated with a framework that subjects validators to a unique eligibility criterion and a combination of reputation, security score, online aging, and general performance indicators related to node reliability, significantly reducing the risk of validator misbehavior and enhancing security, reliability and confidentiality of the entire blockchain compared to other existing approaches.

Contact tracing mobile applications have been emerging as potentially automating surveillance technology to help stem the spread of the novel coronavirus (SARS-COV-2) by tracking individuals and those they come into exposure with. The avalanche of these apps left the software security researchers’ with concerns about vulnerabilities in hastily written software. On the other hand, the COVID-19 pandemic has motivated the recent interest of leveraging blockchain for healthcare-related scenarios, including proposing and developing blockchain-based contact tracing apps. Utilizing the cryptographic concepts of blockchain to secure the collected data could help in winning the level of public engagement required to fight the spread of COVID-19. But will blockchain be a panacea to all the challenges accompanying these apps? Motivated by answering this question and following a twofold process, this paper: (i) explores the current landscape of contact tracing mobile apps, (ii) examines how blockchain technology can contribute positively to this landscape, and (iii) reports on the technical and social challenges that still accompany the deployment of blockchain-based contact tracing apps.

The Internet of Things (IoT) and Robotics cannot be considered two separate domains these days. The Internet of Robotics Things (IoRT) is a concept that has been recently introduced to describe the integration of robotics technologies in IoT scenarios. As a consequence, these two research fields have started interacting, and thus linking research communities. In this paper we intend to make further steps in converging the two communities and broaden the discussion on the development of this interdisciplinary field. The paper provides overview, analysis and challenges of possible solutions for the Internet of Robotic Things, discussing the issues of the IoRT architecture, and the integration of smart environments and robotic applications.

[Background] Prior research on the professional occupation of Requirements Engineering (RE) in Europe and Latin America indicated incongruities between RE practice as perceived by industry and as in textbooks, and conducted detailed analysis of both RE and non-RE job aspects. Relatively little is published on the RE competencies and skills industry expects, and seldom investigated the application domains calling for RE professionals. [Aims] We felt motivated by those findings to carry out research on RE job posts in a North-American market. Especially, we focused solely on RE-specific tasks, competencies and skills, from the perspective of defined position categories. Plus, we intend to explore the application domains in need for RE professionals to reveal the wide range of RE roles in industry. [Methods] Coding process, analysis, and synthesis were applied to the textual descriptions of the 190 RE job ads from Canada's most popular online job search site, especially to the text referring to tasks and competencies. [Results] We contribute to the empirical analysis of RE jobs, by providing insights from Canada's IT market in 2017. Using 109 RE job ads from the most popular IT job search portal T-Net, we identified the qualifications, experience and skills demanded by Canadian employers. Furthermore, we explored the distribution of those RE tasks and competences over the 11 categories of RE roles. [Conclusions] Our results suggest that the majority of the employers were big to very big companies in 29 business domains, and the most in-demand RE skills for them were related to RE methods and to project management aspects affecting requirements. In addition, employers placed much more emphasis on experience - both RE-specific and broad software engineering experience, than on higher education.

One of the challenging areas in dealing with Non-Functional Requirements (NFRs) is the need for a decision support system which helps in a better trade-off among alternative operationalizations in the potential solution space for the NFRs. Dealing with such a trade-off is popularly treated with a qualitative fashion through the NFRs framework. In this paper, we propose the incorporation of the mathematical based trade-off technique: Analytical Hierarchy Process (AHP) to quantitatively deal with ambiguities, trade-offs, priorities and interdependencies among NFRs and operationalizations when using the NFRs framework. Such usage of the AHP technique within the NFRs framework will bring the benefits of the mathematically based trade-off techniques to the NFRs framework and eliminates the drawbacks associated with model-based trade-off techniques which the NFRs framework belongs to.

In software industry, many organizations either focus their traceability efforts on Functional Requirements (FRs) or else fail entirely to implement an effective traceability process. Non-Functional Requirements (NFRs) such as security, safety, performance, and reliability are treated in a rather ad hoc fashion and are rarely traced. This is mainly because of the unique nature of NFRs. They are subjective, relative and they tend to become scattered among multiple modules when they are mapped from the requirements domain to the solution space. Furthermore, NFRs can often interact, in the sense that attempts to achieve one NFR can help or hinder the achievement of other NFRs at particular software functionality. Such an interaction creates an extensive network of interdependencies and tradeoffs among NFRs which is not easy to trace. In a previous work, we proposed a conceptualization of NFRs through the NFRs Ontology. In this paper, we extend the previous work by proposing a change management mechanism for tracing the impact of NFRs on the other constructs in the ontology such as FR or NFR operationalization and vice versa, and providing a traceability mechanism using Datalog expressions to implement queries on a relational model-based representation for the ontology. The proposed traceability queries are then evaluated through a multi-project variation quasi-experiment on regression testing conducted in the industry.

The effort estimation techniques used in the software industry often tend to ignore the impact of Non-functional Requirements (NFR) on effort and reuse standard effort estimation models without local calibration. Moreover, the effort estimation models are calibrated using data of previous projects that may belong to problem domains different from the project which is being estimated. Our approach suggests a novel effort estimation methodology that can be used in the early stages of software development projects. Our proposed methodology initially clusters the historical data from the previous projects into different problem domains and generates domain specific effort estimation models, each incorporating the impact of NFR on effort by sets of objectively measured nominal features. We reduce the complexity of these models using a feature subset selection algorithm. In this paper, we discuss our approach in details, and we present the results of our experiments using different supervised machine learning algorithms. The results show that our approach performs well by increasing the correlation coefficient and decreasing the error rate of the generated effort estimation models and achieving more accurate effort estimates for the new projects.

The growing awareness of the importance of non-functional requirements (NFRs) among the requirements engineering (RE) community in the last few years led to a heightened interest in NFRs description and modeling and, in turn, to the emergence of several models intended to capture and structure the relevant concepts defining the NFRs and their relations. Yet, most of the terms and concepts in use for describing NFRs have been loosely defined, and often there is no commonly accepted taxonomy of NFRs. Common Foundation is required to enable effective communication and to enable integration of NFRs' related research activities within the RE community. In this paper, this Common Foundation will be realized by developing an ontology-based approach which is adequate for projects taking into consideration the NFRs and their relations earlier in the software development and throughout the life cycle.

Requirements changes are an issue in the software development life cycle which often originates from an incomplete knowledge of the domain of interest. Hardly any requirement manifests in isolation, and usually the provision of one requirement may affect the level of provision of another. Understanding the relations among system requirements is essential to ensuring their consistency and change management. In practice, many organizations either focus their traceability efforts on functional requirements (FRs) or else fail entirely to implement an effective traceability process. Tracing non-functional requirements (NFRs) has, by and large, been neglected. In this paper, we propose a metamodel which explicitly captures NFRs and their relations, and which is independent from any programming paradigm. In addition, we present an implementation using XML-based representations for the metamodel and XQuery queries to represent tracing information.

Adopting aspect-oriented technologies for software development requires revisiting the entire traditional software lifecycle in order to identify and represent occurrences of crosscutting during software requirements engineering and design, and to determine how concerns are composed. In this work, we propose sets of quality measurements to be associated with the activities of aspect-oriented software development (AOSD). The intended goal of the measurements is to assist stakeholders with quantitative evidences to better map or iterate system modules at different activities in the development process and to better set the design decisions for the analyzed requirements.