AI-driven recruitment systems offer potential benefits in improving hiring efficiency and reach. However, without explicit attention to inclusivity, these systems risk perpetuating historical biases and reinforcing systemic inequalities. While ethical guidelines for inclusive AI exist, organisations face challenges in operationalising them, particularly in complex, real-world settings like online employment platforms. This paper investigates how inclusive design principles, grounded in diversity and inclusion guidelines, can be translated into requirements for AI-driven recruitment systems. We partnered with SEEK, a leading employment marketplace in the Asia-Pacific region, to conduct a co-design workshop focused on identifying and contextualising inclusivity requirements from AI-based hiring use cases. Using requirements engineering techniques of user stories, personas, and stakeholder reflection, we explored two use cases and conducted follow-up interviews six months later to evaluate the impact and sustainability of these efforts. Our findings reveal challenges in aligning inclusivity with business priorities, navigating trade-offs, and addressing data limitations. We present industry-informed lessons on how requirements engineering can support the operationalisation of inclusive AI principles, offering guidance for practitioners aiming to embed diversity and inclusion principles in AI systems. This work contributes to integrating ethical design into AI development lifecycles through stakeholder-driven requirements processes.

Abstract The convergence of Quantum Computing (QC), Quantum Software Engineering (QSE), and Artificial Intelligence (AI) presents transformative opportunities across various domains. However, existing methodologies inadequately address the ethical, security, and governance challenges arising from this technological shift. This paper highlights the urgent need for interdisciplinary collaboration to embed ethical principles into the development of Quantum AI (QAI) and QSE, ensuring transparency, inclusivity, and equitable global access. Without proactive governance, there is a risk of deepening digital inequalities and consolidating power among a select few. We call on the software engineering community to actively shape a future where responsible QSE and QAI are foundational for ethical, accountable, and socially beneficial technological progress.

The rapid expansion of Artificial Intelligence (AI) technologies has introduced both significant advancements and challenges, with diversity and inclusion (D&I) emerging as a critical concern. Addressing D&I in AI is essential to reduce biases and discrimination, enhance fairness, and prevent adverse societal impacts. Despite its importance, D&I considerations are often overlooked, resulting in incidents marked by built-in biases and ethical dilemmas. Analyzing AI incidents through a D&I lens is crucial for identifying causes of biases and developing strategies to mitigate them, ensuring fairer and more equitable AI technologies. However, systematic investigations of D&I-related AI incidents are scarce. This study addresses these challenges by identifying and understanding D&I issues within AI systems through a manual analysis of two AI incident databases, AI Incident Database (AIID) and AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC). The research develops a decision tree to investigate D&I issues tied to AI incidents and populate a public repository of D&I-related AI incidents. The decision tree was validated through a card sorting exercise and focus group discussions. The research demonstrates that almost half of the analyzed AI incidents are related to D&I, with a notable predominance of racial, gender, and age discrimination. The decision tree and resulting public repository aim to foster further research and responsible AI practices, promoting the development of inclusive and equitable AI systems.

Artificial Intelligence (AI) systems have gained significant traction in the recent past, creating new challenges in requirements engineering (RE) when building AI software systems. RE for AI practices have not been studied much and have scarce empirical studies. Additionally, many AI software solutions tend to focus on the technical aspects and ignore human-centered values. In this paper, we report on a case study for eliciting and modeling requirements using our framework and a supporting tool for human-centred RE for AI systems. Our case study is a mobile health application for encouraging type-2 diabetic people to reduce their sedentary behavior. We conducted our study with three experts from the app team - a software engineer, a project manager and a data scientist. We found in our study that most human-centered aspects were not originally considered when developing the first version of the application. We also report on other insights and challenges faced in RE for the health application, e.g., frequently changing requirements.

Abstract Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.

Abstract Software Engineering (SE) as a field has grown significantly in terms of the research publications in the last few years. Criterion‐based assessment of research performance of scholars, countries along with collaboration networks and links among articles are better judged using bibliometric analysis. The results proposed in this work are based on quantitative analysis and data visualisation of 150,087 scholarly articles published in last 13 years (2007–2019) across 85 research areas based on seven categories by providing a detailed and distinctive time‐frame‐based comparison to observe a shift in research trend in SE and its 12 distinguished sub‐areas. The results of the observations proposed in this work include analysis on types of documents, yearly publication trends with results suggesting a prominent increment for the period (2015–2017) in terms of research publications, languages of research publications with findings indicating that there are articles published in languages other than English language, also, publication count‐based rankings of authors, collaboration networks of countries with findings showing the supremacy of USA and China and keyword statistics.

In traditional approaches to building software systems (that do not include an Artificial Intelligent (AI) or Machine Learning (ML) component), Requirements Engineering (RE) activities are well-established and researched. However, building software systems with one or more AI components may depend heavily on data with limited or no insight into the system’s workings. Therefore, engineering such systems poses significant new challenges to RE. Our search showed that literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). Our study’s main objective was to investigate current approaches in writing requirements for AI/ML systems, identify available tools and techniques used to model requirements, and find existing challenges and limitations. We performed a Systematic Literature Review (SLR) of current RE4AI methods and identified 27 primary studies. Using these studies, we analysed the key tools and techniques used to specify and model requirements and found several challenges and limitations of existing RE4AI practices. We further provide recommendations for future research, based on our analysis of the primary studies and mapping to industry guidelines in Google PAIR). The SLR findings highlighted that present RE applications were not adaptive to manage most AI/ML systems and emphasised the need to provide new techniques and tools to support RE4AI.

To complement the manual contact-tracing methods, a flood of coronavirus-related apps was launched in the first half of 2020. Despite the incredible promises made by the governments, contact-tracing apps did not live up to expectations. We provide a contextual perspective of the government commissioned contact-tracing apps from four countries to understand the non-functional requirements (NFRs) and socio-technical factors that hindered the success of these apps. We collected the user reviews from the app stores for iOS and Android versions and identified top news articles related to each app. Our analysis revealed that the dominant factors behind the negligible success of these apps are complex and entangled with the cultural and political dimensions rather than being just technical. The multilayer diversity of the target users also impacted the design and development of contact-tracing apps in an extremely challenging situation. This perspective paper brings into light important elements, such as politics and socio-cultural aspects that should be studied in the design of contact-tracing apps, and public apps in general.

The core aim of requirements inspection is to ensure the high quality of already elicited requirements in the Software Requirements Specification. Teaching requirements inspection to novices is challenging, as inspecting requirements needs several skills as well as knowledge of the product and process that is hard to achieve in a classroom environment. Published studies about pedagogical design specifically for teaching requirements inspection are scarce. Our objective is to present the design and evaluation of a postgraduate course for requirements inspection training. We conducted an empirical study with 138 postgraduate students, teamed up in 34 groups to conduct requirements inspection. We performed qualitative analysis on the data collected from students' reflection reports to assess the effects of the pedagogical design in terms of benefits and challenges. We also quantitatively analyze the correlation between the students' performance in conducting inspections and their ability of writing specifications. From the analysis of students' reflections, several themes emerged such as their difficulty of working with limited information, but also revealed the benefits of learning teamwork and writing good requirements. This qualitative analysis also provides recommendations for improving the related activities. The results revealed a moderate positive correlation between the performance in writing specification and inspection.

Interviews are largely used in the practice of requirements elicitation. Nevertheless, performing an effective interview often depends on soft-skills, and on knowledge acquired through experience. When it comes to requirements engineering education and training (REET), limited resources and few well-founded pedagogical approaches are available to allow students to acquire and improve their skills as interviewers. This paper presents a novel pedagogical approach that combines role-playing, peer-review and self-assessment to enable students to reflect on their mistakes, and improve their interview skills. We evaluate the approach through a controlled quasi-experiment. The study shows that the approach significantly reduces the amount of mistakes made by the students. Feedback from the participants confirms the usefulness and easiness of the proposed training. This work contributes to the body of knowledge of REET with an empirically evaluated method for teaching inter-views. Furthermore, we share the pedagogical material used, to enable other educators to apply and possibly tailor the approach.

[CONTEXT] Involving users in software development is a complex and multi-faceted concept. Empirical research that studies power and politics of user involvement in software development is scarce. [OBJECTIVE] In this paper, we present the results from a case study of a software development project, where organizational politics was explored in context of user involvement in software development. [METHOD] We collected data through 30 interviews with 20 participants, attending workshops, observing project meetings, and analysing projects documents. The qualitative data was rigorously and iteratively analyzed. [RESULTS] The results indicate that the politics was a significant factor used to exert power and influence in decision-making processes. Communication channels were exploited for political purposes. These contributed to the users' dissatisfaction with their involvement thus impacting on the project outcome. [CONCLUSION] Having multiple teams of stakeholders with different levels of power in decision-making, the politics is inevitable and inescapable. Without careful attention, the political aspect of user involvement in software development can contribute to unsuccessful project.

[Context] Interviews are the most widely used elicitation technique in requirements engineering. However, conducting effective requirements elicitation interviews is challenging, due to the combination of technical and soft skills that requirements analysts often acquire after a long period of professional practice. Empirical evidence about training the novices on conducting effective requirements elicitation interviews is scarce. [Objectives] We present a list of most common mistakes that novices make in requirements elicitation interviews. The objective is to assist the educators in teaching interviewing skills to student analysts. [Re-search Method] We conducted an empirical study involving role-playing and authentic assessment with 110 students, teamed up in 28 groups, to conduct interviews with a customer. One re-searcher made observation notes during the interview while two researchers reviewed the recordings. We qualitatively analyzed the data to identify the themes and classify the mistakes. [Results and conclusion] We identified 34 unique mistakes classified into 7 high level themes. We also give examples of the mistakes made by the novices in each theme, to assist the educationists and trainers. Our research design is a novel combination of well-known pedagogical approaches described in sufficient details to make it re-peatable for future requirements engineering education and training research.

Context: User involvement is generally considered to contributing to user satisfaction and project success and is central to Agile software development. In theory, the expectations about user involvement, such as the PO's, are quite demanding in this Agile way of working. But what are the expectations seen in practice, and are the expectations of user involvement aligned among the development team and users? Any misalignment could contribute to conflict and miscommunication among stakeholders that may result in ineffective user involvement. Objective: Our aim is to compare and contrast the expectations of two stakeholder groups (software development team, and software users) about user involvement in order to understand the expectations and assess their alignment. Method: We have conducted an exploratory case study of expectations about user involvement in an Agile software development. Qualitative data was collected through interviews to design a novel method for the assessing the alignment of expectations about user involvement by applying Repertory Grids (RG). Results: By aggregating the results from the interviews and RGs, varying degrees of expectation alignments were observed between the development team and user representatives. Conclusion: Alignment of expectations can be assessed in practice using the proposed RG instrument and can reveal misalignment between user roles and activities they participate in Agile software development projects. Although we used RG instrument retrospectively in this study, we posit that it could also be applied from the start of a project, or proactively as a diagnostic tool throughout a project to assess and ensure that expectations are aligned.

Context: The benefits of involving users in software development projects have been studied extensively in the last four decades and have been reported to contribute to user satisfaction thus leading to system success. However, the relationship between user involvement and system success, being a multi-faceted and complex concept, has introduced many problems and challenges for the practitioners. Objective: In this paper we present our findings from a case study to give a deeper understanding of the challenges and problems of user involvement during software development. Method: The data in the case study was collected from interviews, observations and project documents. Results: We present our results in four main categories related to users, communicative aspects, managerial considerations, and project issues. It was observed that system success is achievable even when there are problems and challenges in involving users. Conclusion: Understanding the nature of the problems related to user involvement helps the project managers to develop appropriate strategies for increasing the effectiveness of user involvement.

For analysts the alignment between the requirements and the available services presents a significant challenge in service oriented paradigm. To address this challenge various technical solutions have already been proposed. Although technical issues play an important role in this selection but organizational and social factors are equally as important in selecting an optimally aligned service for a specific requirement. The users of services are mostly ignored in the alignment process. User feedback analysis has recently gained a lot of research focus, but these benefits have not been fully explored and utilized in service oriented software development. In this paper I present a method for aligning services to requirements that is designed using the Situational Method Engineering approach and it incorporates user feedback about the services. This feedback assists the analysts in extracting required information for making informed decisions while selecting services among available options that satisfies both the user requirements and customer preferences. The method is supported by a proposed tool. The method and the supporting tool will be validated by a controlled experiment and focus group feedback from the practitioners.

Context: In the last four decades involving users in the software development process is claimed to have a positive impact on the success of that software. However, previous reviews on this topic have produced conflicting results. Objectives: Our aim is to present a review on user involvement in software development process and investigate its relationship to software system success. Methods: For our exploration, we performed a Systematic Literature Review using the guidelines provided in the Evidence Based Software Engineering literature. Results: 87 relevant empirical studies were selected and reviewed that investigate various perspectives and concepts of user involvement in software development process during the period of 1980--2012. Among 87 studies reviewed, 59 report that user involvement positively contributes to system success, 7 suggest a negative contribution and 21 are uncertain. Conclusions: Our results show an overall positive impact of user involvement on system success. It also suggests that the relationship between user involvement and system success is neither direct nor simple, and it depends on many different factors and conditions surrounding systems development processes.

Context: The use of Systematic Literature Review (SLR) requires expertise and poses many challenges for novice researchers. The experiences of those who have used this research methodology can benefit novice researchers in effectively dealing with these challenges. Objective: The aim of this study is to record the reported experiences of conducting Systematic Literature Reviews, for the benefit of new researchers. Such a review will greatly benefit the researchers wanting to conduct SLR for the very first time. Method: We conducted a tertiary study to gather the experiences published by researchers. Studies that have used the SLR research methodology in software engineering and have implicitly or explicitly reported their experiences are included in this review. Results: Our research has revealed 116 studies relevant to the theme. The data has been extracted by two researchers working independently and conflicts resolved after discussion with third researcher. Findings from these studies highlight Search Strategy, Online Databases, Planning and Data Extraction as the most challenging phases of SLR. Lack of standard terminology in software engineering papers, poor quality of abstracts and problems with search engines are some of the most cited challenges. Conclusion: Further research and guidelines is required to facilitate novice researchers in conducting these phases properly.

Causes of requirement change - A systematic literature review

Service Oriented Architecture (SOA) is a shift of paradigm in software development. It can be seen as an evolution of Component Based Software Development (CBSD), with web services used instead of Commercial Off-the-shelf (COTS) software. For the last few years the number of services on the web has increased exponentially. Among available services locating the best service that fulfills the user requirement is a challenging task for researchers. There is still no standard Requirement Engineering (RE) process defined for Service Oriented Software Development (SOSD). The traditional processes and those used for COTS selection cannot be used due to the architectural differences of SOSD with other domains. In this paper we have extracted a list of issues and challenges from literature under considerations by research community for RE process in SOSD. The issues of RE in CBSD are compared with those of SOSD, as CBSD is considered close in nature to SOSD. The results shows that there is a need of standard RE process for SOSD with proper guidance on how to perform different steps with details.