Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. Fine-grained emotion classification is thus needed to better understand users’ affective responses and support downstream tasks such as feature-emotion analysis, user-oriented release planning, and issue triaging. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik’s emotion taxonomy to app reviews by developing a structured annotation framework and dataset. Through an iterative human annotation process, we define clear annotation guidelines and document key challenges in emotion classification. Additionally, we evaluate the feasibility of automating emotion annotation using large language models, assessing their cost-effectiveness and agreement with human-labelled data. Our findings reveal that while large language models significantly reduce manual effort and maintain substantial agreement with human annotators, full automation remains challenging due to the complexity of emotional interpretation. This work contributes to opinion mining in requirements engineering by providing structured guidelines, an annotated dataset, and insights for developing automated pipelines to capture the complexity of emotions in app reviews.

Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt engineering, model fine-tuning, and retrieval augmented generation. However, these methods often treat models as isolated black boxes - relying on single-pass outputs without iterative refinement or collaboration, limiting robustness and adaptability. Objective: We propose that, just as human debates enhance accuracy and reduce bias in RE tasks by incorporating diverse perspectives, different LLM agents debating and collaborating may achieve similar improvements. Our goal is to investigate whether Multi-Agent Debate (MAD) strategies can enhance RE performance. Method: We conducted a systematic study of existing MAD strategies across various domains to identify their key characteristics. To assess their applicability in RE, we implemented and tested a preliminary MAD-based framework for RE classification. Results: Our study identified and categorized several MAD strategies, leading to a taxonomy outlining their core attributes. Our preliminary evaluation demonstrated the feasibility of applying MAD to RE classification. Conclusions: MAD presents a promising approach for improving LLM accuracy in RE tasks. This study provides a foundational understanding of MAD strategies, offering insights for future research and refinements in RE applications.

Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from these reviews is key to support analysis on the acceptance of these features, identification of relevant new feature requests and prioritization of feature development, among others. Traditional methods focus on syntactic pattern-based approaches, typically context-agnostic, evaluated on a closed set of apps, difficult to replicate and limited to a reduced set and domain of apps. Mean-while, the pervasiveness of Large Language Models (LLMs) based on the Transformer architecture in software engineering tasks lays the groundwork for empirical evaluation of the performance of these models to support feature extraction. In this study, we present T-FREX, a Transformer-based, fully automatic approach for mobile app review feature extraction. First, we collect a set of ground truth features from users in a real crowdsourced software recommendation platform and transfer them automatically into a dataset of app reviews. Then, we use this newly created dataset to fine-tune multiple LLMs on a named entity recognition task under different data configurations. We assess the performance of T- FREX with respect to this ground truth, and we complement our analysis by comparing T- FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T- FREX through an external human evaluation. Results show that T- FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

Service-oriented computing and context-aware computing are two consolidated paradigms that are changing the way of providing and consuming software services. Whilst service-oriented computing is based on service-oriented architectures for providing flexible software services, context-aware computing articulates different phases of a context life cycle for changing the behavior of such services. The synergy between both paradigms provides the context to this study. This study analyzes the current state of the art of context models, specifically: (1) which are these proposals and how are they related; (2) what are their structural characteristics; (3) what context information is the most addressed; and (4) what are their most consolidated definitions. Given their dominance on the field, the study focuses on ontology-based approaches. We conducted a systematic mapping by establishing a review protocol that integrates automatic and manual searches from different sources. We applied a rigorous method to elicit the keywords from the research questions and selection criteria to retrieve the papers to evaluate. Overall, 138 primary studies were selected to answer our research questions. These proposals were studied in depth by analyzing: 1) distribution along time and their relationships; 2) size correlated with the number of classes and levels of the context model, and coverage of the definitions provided as indicator of quality provided; 3) most addressed context information; 4) most consolidated definitions of context information. The contribution of this survey is to make available a unified and consolidated body of knowledge on context for service-oriented computing that could be instantiated and used as starting point in a variety of use cases. This sweeping view on the anatomy of context models may help avoiding the postulation of new proposals not aligned with the current research.

Self-adaptive systems are capable of dealing with uncertainty at runtime handling complex issues as resource variability, changing user needs, and system intrusions or faults. If the requirements depend on context, runtime uncertainty will affect the execution of these contextual requirements. This work presents SACRE, a proof-of-concept implementation of an existing approach, ACon, developed by researchers of the Univ. of Victoria (Canada) in collaboration with the UPC (Spain). ACon uses a feedback loop to detect contextual requirements affected by uncertainty and data mining techniques to determine the best operationalization of contexts on top of sensed data. The implementation is placed in the domain of smart vehicles and the contextual requirements provide functionality for drowsy drivers.

Nowadays services as those provided by smart cities, health smart services, as well as common services (e.g., telephonic services, e-mail services), have a great economic impact for organisations and represent an important mean to deliver value to their consumers. The malfunctions of both the services themselves as well as the entities responsible for their execution and consumption might cause economic losses, consumers' dissatisfaction and even shorten the service life cycle, among other risks. To avoid malfunctions beyond maintaining quality levels desired, it is important to take into account the widest possible context information that cause either positive or negative effects around services and entities involved in their provisioning and consumption. In this paper, we propose an upper ontology for service provisioning and consumption from a service-centric perspective. Specifically, we focus on software services, although we could argue for more generic applications. The contribution is the analysis, evaluation and reuse of existing proposals on context models to identify the strengths and weaknesses of its current status as well as to identify contexts not yet considered, and consolidate an integrated view of these proposals. The ultimate intention is to provide a well-defined and consolidated infrastructure of context information as a common body of knowledge, that could be instantiated on variety of use cases, for example, to be instantiated by monitors as context information useful to be monitored, or to be used as context information that allows knowing which contexts affect a service when a user consumes it, among others.

The quality of Open Source Software products is directly related to its community's health. To date, health analysis is made accessing available data repositories or using software management tools that are often too static or ad hoc. To address this issue, we propose to adopt principles and methods from the Service Oriented Computing field. Particularly, we propose to adapt the concepts of quality service and service level agreement, and propose to reuse the existing body of knowledge and techniques from SOC monitoring. To demonstrate the feasibility of the approach, we use a service monitoring framework called SALMonOSS as a proof of concept to realize the implementation of the proposal.

Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems consumers and providers. Since the violation of such SLAs may involve penalties, quality assurance techniques have been developed to supervise the SLAs fulfillment at runtime. However, existing proposals present some drawbacks: 1) the SLAs they support are not expressive enough to model real-world scenarios, 2) they couple the monitoring configuration to a given SLA specification, 3) the explanations of the violations are difficult to understand and even potentially inaccurate, 4) some proposals either do not provide an architecture, or present low cohesion within their elements. In this paper, we propose a comprehensive solution, from a conceptual reference model to its design and implementation, that overcomes these drawbacks. The resulting platform, SALMonADA, receives the SLA agreed between the parties as input and reports timely and comprehensive explanations of SLA violations. SALMonADA performs an automated monitoring configuration and it analyses highly expressive SLAs by means of a constraint satisfaction problems based technique. We have evaluated the impact of SALMonADA over the resulting service consumption time performance. The results are satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness.

Cloud Computing offers simple and cost effective outsourcing in dynamic service environments, and allows the construction of service-based applications using virtualization. By aggregating the capabilities of various IaaS cloud providers, federated clouds can be built. Managing such a distributed, heterogeneous environment requires sophisticated interoperation of adaptive coordinating components. In this paper we introduce an integrated federated management and monitoring approach that enables autonomous service provisioning in federated clouds. In this architecture, cloud brokers manage the number and the location of the utilized virtual machines for the received service requests. In order to provide seamless service executions, a state of the art monitoring solution is proposed that supports cloud selection performed by the management layer of the architecture. Our solution is able to cope with highly dynamic service executions by federating heterogeneous cloud infrastructures in a transparent and autonomous manner.

Increasingly, service-based applications (SBAs) are composed of third-party services available over the Internet. Even if third-party services have shown to work during design-time, they might fail during the operation of the SBA due to changes in their implementation, provisioning, or the communication infrastructure. As a consequence, SBAs need to dynamically adapt to such failures during run-time to ensure that they maintain their expected functionality and quality. Ideally the need for an adaptation is proactively identified, i.e., failures are predicted before they can lead to consequences such as costly compensation and roll-back activities. Currently, approaches to predict failures are based on monitoring. Due to its passive nature, however, monitoring might not cover all relevant service executions, which can diminish the ability to correctly predict failures. In this paper we demonstrate how online testing, as an active approach, can improve failure prediction by considering a broader range of service executions. Specifically, we introduce a framework and prototypical implementation that exploits synergies between monitoring, online testing and quality prediction. For online test selection and assessment we adapt usage-based testing strategies. We experimentally evaluate the strengths of our approach in predicting the need for an adaptation of an SBA.