Tony Gorschek

Oleksandr Kosenkov, Konstantin Blaschke, Tony Gorschek et al.

There is a growing demand for software engineering education (SEE) for professionals because of the increasing demand, active evolution of the technological landscape, and changes in the skills required by the practice. Integrating requirements engineering (RE) courses into SEE curricula for professionals systematically and effectively is challenging. In particular, curricula for professionals have different demands, are more dynamic, and modular in nature. In this study, we report on our experience in the development of three SEE curricula for professionals and the integration of RE courses into such curricula. We suggest basic principles for such integration and describe the systematic approach focused on course content mapping that we have developed.

Anna Kusetogullari, Huseyin Kusetogullari, Martin Andersson et al.

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of their impact on the preconditions for entrepreneurship. There is still a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analyzing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyze 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation and Behavioral Models, (2) GenAI-Enhanced Education and Learning Systems, (3) Sustainable Innovation and Strategic AI Impact, (4) Business Models and Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature, as well as ethical concerns raised in the literature. We highlight the need for more macro-level research on GenAI and LLMs as external enablers for entrepreneurship and for research on effective regulatory frameworks that facilitate business experimentation, innovation, and further technology development.

Marcos Kalinowski, Daniel Mendez, Görkem Giray et al.

Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic research. Method: We conducted an international survey to collect insights from practitioners on the current practices and problems in engineering ML-enabled systems. We received 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems using open and axial coding procedures. Results: Our survey results reinforce and extend existing empirical evidence on engineering ML-enabled systems, providing additional insights into typical ML-enabled systems project contexts, the perceived relevance and complexity of ML life cycle phases, and current practices related to problem understanding, model deployment, and model monitoring. Furthermore, the qualitative analysis provides a detailed map of the problems practitioners face within each ML life cycle phase and the problems causing overall project failure. Conclusions: The results contribute to a better understanding of the status quo and problems in practical environments. We advocate for the further adaptation and dissemination of software engineering practices to enhance the engineering of ML-enabled systems.

Florian Angermeir, Maximilian Amougou, Mark Kreitz et al.

Large Language Models have gained remarkable interest in industry and academia. The increasing interest in LLMs in academia is also reflected in the number of publications on this topic over the last years. For instance, alone 78 of the around 425 publications at ICSE 2024 performed experiments with LLMs. Conducting empirical studies with LLMs remains challenging and raises questions on how to achieve reproducible results, for both researchers and practitioners. One important step towards excelling in empirical research on LLM and their application is to first understand to what extent current research results are eventually reproducible and what factors may impede reproducibility. This investigation is within the scope of our work. We contribute an analysis of the reproducibility of LLM-centric studies, provide insights into the factors impeding reproducibility, and discuss suggestions on how to improve the current state. In particular, we studied the 85 articles describing LLM-centric studies, published at ICSE 2024 and ASE 2024. Of the 85 articles, 18 provided research artefacts and used OpenAI models. We attempted to replicate those 18 studies. Of the 18 studies, only five were sufficiently complete and executable. For none of the five studies, we were able to fully reproduce the results. Two studies seemed to be partially reproducible, and three studies did not seem to be reproducible. Our results highlight not only the need for stricter research artefact evaluations but also for more robust study designs to ensure the reproducible value of future publications.

Razieh Dehghani, Krzysztof Wnuk, Daniel Mendez et al.

Context and Motivation: Software requirements are affected by the knowledge and confidence of software engineers. Analyzing the interrelated impact of these factors is difficult because of the challenges of assessing knowledge and confidence. Question/Problem: This research aims to draw attention to the need for considering the interrelated effects of confidence and knowledge on requirements quality, which has not been addressed by previous publications. Principal ideas/results: For this purpose, the following steps have been taken: 1) requirements quality was defined based on the instructions provided by the ISO29148:2011 standard, 2) we selected the symptoms of low qualified requirements based on ISO29148:2011, 3) we analyzed five Software Requirements Specification (SRS) documents to find these symptoms, 3) people who have prepared the documents were categorized in four classes to specify the more/less knowledge and confidence they have regarding the symptoms, and 4) finally, the relation of lack of enough knowledge and confidence to symptoms of low quality was investigated. The results revealed that the simultaneous deficiency of confidence and knowledge has more negative effects in comparison with a deficiency of knowledge or confidence. Contribution: In brief, this study has achieved these results: 1) the realization that a combined lack of knowledge and confidence has a larger effect on requirements quality than only one of the two factors, 2) the relation between low qualified requirements and requirements engineers' needs for knowledge and confidence, and 3) variety of requirements engineers' needs for knowledge based on their abilities to make discriminative and consistent decisions.

Cristina Palomares, Xavier Franch, Carme Quer et al.

Context. Requirements engineering remains a discipline that is faced with a large number of challenges, including the implementation of a requirements elicitation process in industry. Although several proposals have been suggested by researchers and academics, little is known of the practices that are actually followed in industry. Objective. We investigate the SoTA with respect to requirements elicitation, examining practitioners' practices. We focus on the techniques, the roles involved, and the challenges associated to the process. Method. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. Results. We found that group interaction techniques, including meetings and workshops, are the most popular type of elicitation techniques that are employed by the practitioners, except in the case of small projects. We noted that customers are frequently involved in the elicitation process, except in the case of market-driven organizations. Technical staff (for example, developers and architects) are more frequently involved in the elicitation process compared to the involvement of business- or strategic staff. Finally, we identified a number of challenges with respect to stakeholders. These challenges include difficulties in understanding and prioritizing their needs. Further, it was noted that requirements instability (i.e., caused by changing needs or priorities) was a predominant challenge. These observations need to be interpreted in the context of the study. Conclusion. The relevant observations regarding the survey participants' experiences should be of interest to the industry; experiences that should be analyzed in the practitioners' context. Researchers may find evidence for the use of academic results in practice, thereby inspiring future theoretical work, as well as further empirical studies in the same area.

Ehsan Zabardast, Javier Gonzalez-Huerta, Tony Gorschek et al.

Context: Developing software-intensive products or services usually involves a plethora of software artefacts. Assets are artefacts intended to be used more than once and have value for organisations; examples include test cases, code, requirements, and documentation. During the development process, assets might degrade, affecting the effectiveness and efficiency of the development process. Therefore, assets are an investment that requires continuous management. Identifying assets is the first step for their effective management. However, there is a lack of awareness of what assets and types of assets are common in software-developing organisations. Most types of assets are understudied, and their state of quality and how they degrade over time have not been well-understood. Method: We perform a systematic literature review and a field study at five companies to study and identify assets to fill the gap in research. The results were analysed qualitatively and summarised in a taxonomy. Results: We create the first comprehensive, structured, yet extendable taxonomy of assets, containing 57 types of assets. Conclusions: The taxonomy serves as a foundation for identifying assets that are relevant for an organisation and enables the study of asset management and asset degradation concepts.

Ehsan Zabardast, Julian Frattini, Javier Gonzalez-Huerta et al.

During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product's final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets' characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.

Thomas Olsson, Krzystof Wnuk, Tony Gorschek

[Context] Quality requirements are important for product success yet often handled poorly. The problems with scope decision lead to delayed handling and an unbalanced scope. [Objective] This study characterizes the scope decision process to understand influencing factors and properties affecting the scope decision of quality requirements. [Method] We studied one company's scope decision process over a period of five years. We analyzed the decisions artifacts and interviewed experienced engineers involved in the scope decision process. [Results] Features addressing quality aspects explicitly are a minor part (4.41%) of all features handled. The phase of the product line seems to influence the prevalence and acceptance rate of quality features. Lastly, relying on external stakeholders and upfront analysis seems to lead to long lead-times and an insufficient quality requirements scope. [Conclusions] There is a need to make quality mode explicit in the scope decision process. We propose a scope decision process at a strategic level and a tactical level. The former to address long-term planning and the latter to cater for a speedy process. Furthermore, we believe it is key to balance the stakeholder input with feedback from usage and market in a more direct way than through a long plan-driven process.

Raquel Andrade Barros Ouriques, Krzysztof Wnuk, Tony Gorschek et al.

Knowledge-intensive companies that adopt Agile Software Development (ASD) relay on efficient implementation of Knowledge Management (KM) strategies to promotes different Knowledge Processes (KPs) to gain competitive advantage. This study aims to explore how companies that adopt ASD implement KM strategies utilizing practices that promote the KPs in the different organizational layers. Through a systematic literature review, we analyzed 32 primary studies, selected by automated search and snowballing in the extant literature. To analyze the data, we applied narrative synthesis. Most of the identified KM practices implement personalization strategies (81 %), supported by codification (19 %). Our review shows that the primary studies do not report KM practices in the strategic layer and two of them in the product portfolio layer; on the other hand, in the project layer, the studies report 33 practices that implement personalization strategy, and seven practices that implement codification. KM strategies in ASD promote mainly the knowledge transfer process with practices that stimulate social interaction to share tacit knowledge in the project layer. As a result of using informal communication, a significant amount of knowledge can be lost or not properly transferred to other individuals and, instead of propagating the knowledge, it remains inside a few individuals minds.