Large language models (LLMs) have enabled new tools in requirements engineering (RE), often in the form of intelligent agents or virtual assistants. These tools can transform how software engineers perform RE tasks and interact with stakeholders. However, existing research primarily focuses on showcasing the capabilities of these tools rather than their design and evaluation in RE-specific contexts. This limits our understanding of their practical value and hinders broader adoption. To address this gap, we propose a reference model to guide the design, use, and evaluation of intelligent RE agents. Our work introduces new RE use cases, along with evaluation metrics for intelligent RE agents. We present a study design to support systematic development and share early findings demonstrating the feasibility of our approach. The use cases show how agents can add value for RE practitioners, while our synthesized catalogue supports tool evaluation. Finally, our analysis of commercial agents reveals that these tools already support certain aspects of the envisioned RE use cases.

App reviews provide a rich source of feature-related information that can support requirement engineering activities. Analyzing them manually to find this information, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for ‘feature-specific analysis’. Unfortunately, the effectiveness of these approaches has been evaluated using different methods and datasets. Replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present two empirical studies. In the first study, we evaluate opinion mining approaches; the approaches extract features discussed in app reviews and identify their associated sentiments. In the second study, we evaluate approaches searching for feature-related reviews. The approaches search for users’ feedback pertinent to a particular feature. The results of both studies show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.

App reviews can provide valuable information about user needs but analyzing them manually is challenging due to their large quantity and noisy nature. To overcome this problem, a variety of app review mining techniques have been proposed. So far, however, research in this area has paid little attention to the software engineering use cases of the mining techniques. This limits the understanding of their usefulness, applications and desired future developments. We address this problem by elaborating a reference model relating app review mining techniques to specific software engineering activities. In this paper, we present a unified description of software engineering use cases for mining app reviews and define a reference architecture realizing these use cases through a combination of natural language processing and data mining techniques. The use cases provide a novel systematic exposition of the envisioned applications and benefits of app review mining for software engineers. The reference architecture synthesises the diversity of research to realise these benefits and provide a general framework guiding the development and evaluation of future research and tools.

Abstract App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

[Context/Motivation]: The strategy of an organization defines its long-term goals and develop plans to achieve these goals. Strategic planning is the activity of deciding how to allocate resources within the organization to satisfy its strategy. Strategic planning precedes more detailed requirements engineering activities that clarify the requirements for the software systems concerned by the strategic plan. [Question/Problem]: Unfortunately, strategic goals are often vague and the IT projects in the strategic plans are sometimes not well aligned with these goals and not flexible enough to deal with uncertainty and changing conditions. [Previous work/Limitations]: Goal-oriented requirements engineering methods have been proposed to help strategic decision-making. These methods include various qualitative and quantitative techniques, but they lack support for deciding the timings of IT initiatives, for rigorously collecting and analyzing data to support strategic decisions, and for revising strategic plans based on changing conditions and past knowledge. [Principal ideas/Approach]: The objective of my research is to extend goal-oriented requirements engineering techniques to provide such support in the context of strategic planning activities. My approach will involve designing an adaptive framework with support for goal modelling and reasoning about the timing of IT initiatives and for enabling data-driven decisions using machine learning and Bayesian decision analysis techniques. [Contribution]: This paper presents a partial example illustrating the problems I wish to address, preliminary ideas on how to solve them, and a research roadmap to achieve my research objectives.

This paper presents a physical human-robot interaction (pHRI) interface, which enables the user to control a walking humanoid robot through physical contact. A human operator guides the robot in parent-child-like behavior by exerting force onto the hand of the robot. In contrast to a conventional approach of pHRI in which force/torque measurements are applied, the proposed solution is based on sensorless force control. Furthermore, we demonstrate an extension to the generic interface by implementing a number of gait control algorithms. This paper also evaluates user-acceptable robot responses while guiding a humanoid robot walking. User opinion on the presented control solution is evaluated through usability testing conducted among prospective users. The obtained results indicate that the developed interfaces present an effective solution to the problem of sensorless guidance of a walking robot.