The development of high-quality software depends on precise and comprehensive requirements that meet the objectives of stakeholders. Goal modeling techniques have been developed to fill this gap by capturing and analyzing stakeholders' needs and allowing them to make trade-off decisions; yet, goal modeling analysis is often difficult for stakeholders to interpret. Recent work found that when subjects are given minimal training on goal modeling and access to a color visualization, called EVO, they are able to use EVO to make goal modeling decisions faster without compromising quality. In this paper, we evaluate the robustness of the empirical evidence for EVO and question the underlying color choices made by the initial designers of EVO. We conduct a pseudo-exact replication$(n=60)$of the original EVO study, varying the experimental site and the study population. Even in our heterogeneous sample with less a priori familiarity with requirements and goal modeling, we find that individuals using EVO answered the goal-modeling questions significantly faster than those using the control, expanding the external validity of the original results. However, we find some evidence that the chosen color scheme is not intuitive and make recommendations for the goal modeling community.

The growing number of older adults facing isolation, cognitive decline, and technological exclusion poses critical challenges for the design of inclusive digital systems. Despite increasing research interest in age-inclusive technology, requirements engineering (RE) methods remain largely inadequate for this vulnerable population due to cognitive, linguistic, ethical, and contextual mismatches. This paper identifies four key challenges in applying RE to older adults: variability in cognitive and linguistic capabilities, ethical and privacy risks, fragmented design guidelines, and inconsistencies in requirements elicitation processes. To address these issues, we propose a multidisciplinary framework guided by artificial intelligence (AI) with five interlinked components: a community-driven corpus, age-sensitive elicitation techniques, emotionally intelligent tools for requirement creation, ethical awareness indicators, and inclusive validation processes. AI methods such as natural language processing, risk modeling, and adaptive interface analysis are embedded throughout the framework to personalize, automate, and validate RE processes for older adults. Our vision aims to foster technologies that are accessible, respectful, emotionally resonant, and practically effective for aging users, bridging the gap between RE research and real-world systems that empower older adults.

Formal methods for requirements engineering have existed for decades; yet, these techniques are rarely used if not required by certification because they are challenging for non-experts (e.g., novices and non-technical stakeholders in multidisciplinary teams) to interpret and apply. To enable non-experts to participate in collaborative software teams, we envision using artificial intelligence (AI) to assist in interpreting formal notations. Our research project investigates how and to what extent generative AI with large language models (LLMs) can be used to assist non-experts in interpreting formal requirements. In this paper, we conduct an exploratory investigation of both generating translations and interpreting linear temporal logic (LTL) formulae. Specifically, we explore prompting LLMs with sufficient information for the task of generating LTL formula explanations. With our initial prompt, we complete a classroom study where students learn LTL and then interpret a series of LTL formulae with and without the LLM-generated descriptions. We then improve our approach based on insights from the classroom study, and evaluate the overall quality of our updated prompt and the explanations it generates.

Requirements engineering (RE) is an essential part of the software development process. Good RE, among others, is the basis for high quality software, considerably reduces the risk for software projects to fail entirely or with budget-overspending and is crucial for coordinating systems and software engineering. Thus, RE education is a vital part of software engineering curricula. However, a central concept of what RE education comprise and how to best teach RE is lacking. Therefore, we conducted a systematic literature review of the field and provide a systematic map describing the state of the RE education field. Results for key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills.

Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demon-strated promise in their analytical capabilities; yet, stakeholders have expressed concerns about the readability of the models and resulting analysis, which builds upon Tropos. Tropos is based on formal semantics enabling automated analysis; however, this creates a problem of interpreting evidence pairs. The aim of our broader research project is to improve the process of model comprehension and decision making by improving how analysts interpret and make decisions. We extend and evaluate a prior approach, called EVO, which uses color to visualize evidence pairs. In this scientific evaluation paper, we explore the effectiveness and usability of EVO. We conduct an experiment (n = 32) to measure any effect of using colors to represent evidence pairs. We find that with minimal training, untrained modelers were able to use the color visualization for decision making. The visualization significantly improves the speed of model comprehension and users found it helpful.

Automated analysis has been used in goal-oriented requirements engineering (GORE) to evaluate scenarios and make trade-off decisions. For higher complexity problems (e.g., backwards analysis), using a search-based solver may be more efficient than custom algorithms. When these black-box solvers produce a single solution, users may be suspicious about whether the given answer is ideal or believable. Users would like to explore the potential solutions but are prevented from doing so because these inquiries often suffer from a state explosion problem. In this RE@Next! paper, we introduce the use of valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. We use the concrete semantics of modeling requirements in the Evolving Intentions framework and its associated goal modeling tool, BloomingLeaf, to explore the application of these visualization techniques. In our initial evaluation, we demonstrate how these techniques can be used on a fully worked out example. We conduct initial measurements of the time savings and state space reduction created by the valuations and color filtering, and discuss future directions of this project.

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or budget-overspending of software development projects. It is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. To this date, there exists no central concept of what RE education shall comprise. To lay a foundation, we report on a systematic literature review of the field and provide a systematic map describing the current state of RE education. Doing so allows us to describe how the educational landscape has changed over the last decade. Results show that only a few established author collaborations exist and that RE education research is predominantly published in venues other than the top RE research venues (i.e., in venues other than the RE conference and journal). Key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills. Finally, we discuss open opportunities in RE education, such as training for security requirements and supply chain risk management, as well as developing a pedagogical foundation grounded in evidence of effective instructional approaches.

Goal modeling enables the elicitation of stakeholders’ intentionality in the earlier stages of a project. Often, approaches are limited by the effort required to create an initial goal model. In this paper, we investigate the problem of model merging for Tropos goal models. Specifically, we propose a formal approach to the problem of automatically merging the attributes of intentions and actors, once these elements have been matched. Additionally, recent approaches have investigated answering questions about future evolutions of stakeholders’ projects with goal models. In this work we consider both static models, as well as those with timing information, using the principles of gullibility, contradiction, and consensus. We study our implementation and validate the merge operation on a variety of models from the literature.

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or exceeding budgets of software development projects. Therefore, it is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. However, to date there exists no central concept of what the most useful educational approaches are in RE education in order to best interweave theory with practice. To lay the foundation for this important mission, we conducted a systematic literature review. In this paper, we report on the results and provide a synthesis of instructional approaches in RE education. Findings show that experiential learning through projects, collaboration, and realistic stakeholder involvement are among the most promising trends to teach both RE theory and develop student soft skills.

Software engineers, and more broadly computer scientists, must be able to communicate clearly to exchange ideas with team members and stakeholders, making writing an essential part of computer science education. The Writing Across the Curriculum initiative endeavours to engage students in writing and creating a community that supports writing and its teaching in every discipline. As part of this initiative, computer science (CS) instructors at a large public university added a weekly blogging assignment to their CSII course with four aims: (1) improve student learning of writing and CS concepts; (2) understand local variations among language speakers; (3) provide writing feedback at scale; and (4) combine writing-to-learn with disciplinary writing. In this paper, we present the design rationale for the blog assignment, as well as the results of an empirical study of how the assignment is perceived by students and teaching assistants. Using both quantitative and qualitative analysis, we tease apart the mixed perceptions of the students and the outcomes of our assignment aims. We discuss problems as well as potential opportunities for improving the assignment, and explore recommendations for those interested in teaching writing.

Goal models help stakeholders make trade-off decisions in the early stages of project development. While these approaches have significant analysis capabilities, they have yet to see broad industrial adoption, with the construction of scalable, realistic goal models acting as a significant barrier. Over the last decade, researchers have used force-directed algorithms, specifically GraphViz, to layout goal models and have called for improved layout algorithms to better accommodate the unique challenges presented by actor-based models. We extend a force-directed algorithm to include goal model heuristics, and independently arrived at a domain specific version of a generic layout algorithm for undirected compound graphs. As initial validation of the effectiveness and scalability of our algorithm, we implement our approach in BloomingLeaf, a goal model analysis tool. Initial results are promising; yet, further collaboration and validation across the various goal modeling approaches (e.g., GRL, iStar, Tropos) is required before we can recommend our approach to be adopted in tooling. This paper presents early results and lays a foundation for discussion within our GORE community.

In order to build resilient systems that can be operational for a long time, it is important that analysts are able to model the evolution of the requirements of that system. The Evolving Intentions framework models how stakeholders’ goals change over time. In this work, our aim is to validate applicability and effectiveness of this technique on a substantial case. In the absence of ground truth about future evolutions, we used historical data and rational reconstruction to understand how a project evolved in the past. Seeking a well-documented project with varying stakeholder intentions over a substantial period of time, we selected requirements of the Toronto Spadina Expressway. In this paper, we report on the experience and the results of modeling this project over different time periods, which enabled us to assess the modeling and reasoning capabilities of the approach, its support for asking and answering ‘what if’ questions, and the maturity of the underlying tool support. We also demonstrate a novel process for creating time-based models through the construction and merging of scenarios.

Goal models are used in early phase requirements engineering to elicit stakeholders' intentions, analyze dependencies, and help stakeholders make trade-off decisions about the project and its interaction with the environment. The Evolving Intentions framework extended goal model analysis to evaluate how models change over time, by creating simulation paths showing possible evolutions of the model. More recently, we extended this analysis to allow users to explore states along the path and generate their own simulation paths. However, this approach is limited by users' ability to comprehend the state space, which grows exponentially with the size of the model. In this paper, we explore using filters to reduce the number of viewable solutions enabling users to create their own simulation results. We present our approach and initial validation, including an analysis of prior models and a review of expert feedback.

Goal models have long been used in academia without wide spread adoption in industry. If the fundamental purpose of goal models is to allow stakeholders to generate scenarios and ask "what if" questions, then which parts of the process of model construction, analysis, and evolution benefit from and which are hindered by manual activities? The recent expansion of goal modelling to ask time-based questions further amplifies this issue because significant additional information is required from stakeholders. Through a series of empirical studies, we aim to isolate the processes of model construction, analysis, and evolution for the purpose of studying the utility of goal-oriented requirements engineering approaches and exploring which tasks are essential practices that stakeholders must complete themselves to gain modeling benefit, and which tasks can be simplified through automation. In this process, we will also measure the benefits of completing relevant goal modelling activities with and without timing analysis. In this short communication, we describe our objectives for understanding the benefits of and barriers to goal-oriented requirements engineering.

Our previous work presented the Evolving Intentions framework, which specified how evolving qualitative goal models can be modeled and analyzed. Recent improvements to the framework allow for precise semantics of goal relationships with propagation of both evidence for and evidence against a goal's satisfaction (as in Tropos), and enables evaluation of evolution with absolute time (in addition to relative time). The reasoning is expressed as a constraint satisfaction problem. In this paper, we present BloomingLeaf, a new web-based tool that implements the new semantics. We showcase how the implementation and architecture of BloomingLeaf can be used to answer time-based questions.

Existing modeling approaches in requirements engineering assume that stakeholder goals are static: once set, they remain the same throughout the lifecycle of the project. Of course, such goals, like anything else, may change over time. In earlier work, we introduced Evolving Intentions: an approach that allows stakeholders to specify how evaluations of goal model elements change over time. Simulation over Evolving Intentions enables stakeholders to ask a variety of 'what if' questions, and evaluate possible evolutions of a goal model. GrowingLeaf is a web-based tool that implements both the modeling and analysis components of this approach. In this paper, we investigate the effectiveness and usability of Evolving Intentions, Simulation over Evolving Intentions, and GrowingLeaf. We report on a between-subjects experiment we conducted with fifteen graduate students familiar with requirements engineering. Using qualitative, quantitative, and timing data, we show that Evolving Intentions were intuitive, that Simulation over Evolving Intentions increased the subjects' understanding and produced meaningful results, and that GrowingLeaf was found to be effective and usable.

Goal modeling has long been used in the literature to model and reason about system requirements, constraints within the domain and environment, and stakeholders' goals. Goal model analysis helps stakeholders answer 'what if' questions enabling them to make tradeoff decisions about their project requirements. However, questions concerning the evolution over time of stakeholder requirements or changes in actor intentionality are not explicitly addressed by current approaches. In this paper, we tackle this problem by presenting a method for specifying changes in intentions over time, and a technique that uses simulation for asking a variety of 'what if' questions about such models. Using the development of a web-based modeling tool as an example, we demonstrate that this technique is effective for debugging goal models and answering stakeholder questions.

Goal models for early phase requirements enable modelers to elicit stakeholders' intentions, analyze dependencies and select preferred alternatives. Standard analysis techniques provide options for analysis of static goal models but do not consider the dynamic environment that the model represents and do not evaluate the intentions over time. In this position paper, we illustrate that goal model analysis for early phase requirements can be improved by explicitly considering the intention dynamics of a potential system across multiple time scales.

Software teams often have trouble coordinating shared work due to poor communication practices. We surveyed software engineers (N=989) at Microsoft to investigate three rarely explored aspects of coordination: (1) how an engineer's perception of dependence is predicted by his organizational characteristics, (2) how this perception differs when the dependence varies by the kinds of shared work artifacts, and (3) how the work group range affects the likelihood that an engineer will share information about work artifacts with another. Our results indicate that engineers tailor their communications about shared work for each group of intended recipients. This suggests that many existing coordination tools that rely on automatic mining and visualization of engineering activities have prevented senders from controlling the distribution of information about their work, and may have overestimated the receivers' abilities to comprehend it.