arXiv subjects

Grischa Liebel

Publications and source records attributed to Grischa Liebel.

At least 19 recordsLinked to original sources

arXiv2026

Domain Diversity, Motivation, Inclusion, and Feedback in Software Modelling Education

Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.

cs.SE↗

arXiv2026

Exploring Creativity in Human-Human-LLM Collaborative Software Design

While the use of Large Language Models (LLMs) in programming has been extensively studied, there is limited understanding of how LLMs support collaborative work where creativity plays a central role. Software design, as a collaborative and creative activity, provides a valuable context for exploring the influence of LLMs on creativity. This study investigates how and where creativity naturally emerges when software designers collaborate with an LLM during a design task. In a laboratory setting simulating a workplace environment, 18 pairs of software professionals with design experience were asked to complete a design task. Each pair had 90 minutes to produce a software design based on a set of requirements, with optional access to a custom LLM interface. Pairs were not primed to be creative. We find that creativity was present in all pairs in design processes, with 13 producing design documents containing creativity. We primarily attribute creativity to the human designers, driven by traits such as prior experience, empathy, and the use of analogies. The LLM contributed by producing novel ideas and elaborating human ideas. However, in some cases, the LLM appeared to hinder creativity by suggesting complex solutions or adding to unproductive digressions. LLMs can support creativity in collaborative software design, but human insights remain central. To effectively augment human creativity, designers must be intentional in their engagement with LLMs.

cs.SE↗

arXiv2025

Investigating the Experience of Autistic Individuals in Software Engineering

Context: Autism spectrum disorder (ASD) leads to various issues in the everyday life of autistic individuals, often resulting in unemployment and mental health problems. To improve the inclusion of autistic adults, existing studies have highlighted the strengths these individuals possess in comparison to non-autistic individuals, e.g., high attention to detail or excellent logical reasoning skills. If fostered, these strengths could be valuable in software engineering activities, such for identifying specific kinds of bugs in code. However, existing work in SE has primarily studied the challenges of autistic individuals and possible accommodations, with little attention their strengths. Objective: Our goal is to analyse the experiences of autistic individuals in software engineering activities, such as code reviews, with a particular emphasis on strengths. Methods: This study combines Social-Technical Grounded Theory through semi-structured interviews with 16 autistic software engineers and a survey with 49 respondents, including 5 autistic participants. We compare the emerging themes with the theory by Gama et al. on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance. Results: Our results suggest that autistic software engineers are often skilled in logical thinking, attention to detail, and hyperfocus in programming; and they enjoy learning new programming languages and programming-related technologies. Confirming previous work, they tend to prefer written communication and remote work. Finally, we report a high comfort level in interacting with AI-based systems. Conclusions: Our findings extend existing work by providing further evidence on the strengths of autistic software engineers.

cs.SE↗

arXiv2025

An Empirical Investigation of the Experiences of Dyslexic Software Engineers

Dyslexia is a common learning disorder that primarily impairs an individual's reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring and keeping work. In Software Engineering (SE), reading and writing difficulties appear to pose substantial challenges for core tasks such as programming. However, initial studies indicate that these challenges may not significantly affect their performance compared to non-dyslexic colleagues. Conversely, strengths associated with dyslexia could be particularly valuable in areas like programming and design. However, there is currently no work that explores the experiences of dyslexic software engineers, and puts their strengths into relation with their difficulties. To address this, we present a qualitative study of the experiences of dyslexic individuals in SE. We followed the basic stage of the Socio-Technical Grounded Theory method and base our findings on data collected through 10 interviews with dyslexic software engineers, 3 blog posts and 153 posts on the social media platform Reddit. We find that dyslexic software engineers especially struggle at the programming learning stage, but can succeed and indeed excel at many SE tasks once they master this step. Common SE-specific support tools, such as code completion and linters are especially useful to these individuals and mitigate many of the experienced difficulties. Finally, dyslexic software engineers exhibit strengths in areas such as visual thinking and creativity. Our findings have implications to SE practice and motivate several areas of future research in SE, such as investigating what makes code less/more understandable to dyslexic individuals.

cs.SE↗

arXiv2025

User Misconceptions of LLM-Based Conversational Programming Assistants

Programming assistants powered by large language models (LLMs) have become widely available, with conversational assistants like ChatGPT particularly accessible to novice programmers. However, varied tool capabilities and inconsistent availability of extensions (web search, code execution, retrieval-augmented generation) create opportunities for user misconceptions that may lead to over-reliance, unproductive practices, or insufficient quality control. We characterize misconceptions that users of conversational LLM-based assistants may have in programming contexts through a two-phase approach: first brainstorming and cataloging potential misconceptions, then conducting qualitative analysis of Python-programming conversations from the WildChat dataset. We find evidence that users have misplaced expectations about features like web access, code execution, and non-text outputs. We also note the potential for deeper conceptual issues around information requirements for debugging, validation, and optimization. Our findings reinforce the need for LLM-based tools to more clearly communicate their capabilities to users and empirically ground aspects that require clarification in programming contexts.

cs.HC↗

arXiv2025

Differences between Neurodivergent and Neurotypical Software Engineers: Analyzing the 2022 Stack Overflow Survey

Neurodiversity describes variation in brain function among people, including common conditions such as Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD), and dyslexia. While Software Engineering (SE) literature has started to explore the experiences of neurodivergent software engineers, there is a lack of research that compares their challenges to those of neurotypical software engineers. To address this gap, we analyze existing data from the 2022 Stack Overflow Developer survey that collected data on neurodiversity. We quantitatively compare the answers of professional engineers with ASD (n=374), ADHD (n=1305), and dyslexia (n=363) with neurotypical engineers. Our findings indicate that neurodivergent engineers face more difficulties than neurotypical engineers. Specifically, engineers with ADHD report that they face more interruptions caused by waiting for answers, and that they less frequently interact with individuals outside their team. This study provides a baseline for future research comparing neurodivergent engineers with neurotypical ones. Several factors in the Stack Overflow survey and in our analysis are likely to lead to conservative estimates of the actual effects between neurodivergent and neurotypical engineers, e.g., the effects of the COVID-19 pandemic and our focus on employed professionals.

cs.SE↗

arXiv2025

CoVoL: A Cooperative Vocabulary Learning Game for Children with Autism

Children with Autism commonly face difficulties in vocabulary acquisition, which can have an impact on their social communication. Using digital tools for vocabulary learning can prove beneficial for these children, as they can provide a predictable environment and effective individualized feedback. While existing work has explored the use of technology-assisted vocabulary learning for children with Autism, no study has incorporated turn-taking to facilitate learning and use of vocabulary similar to that used in real-world social contexts. To address this gap, we propose the design of a cooperative two-player vocabulary learning game, CoVoL. CoVoL allows children to engage in game-based vocabulary learning useful for real-world social communication scenarios. We discuss our first prototype and its evaluation. Additionally, we present planned features which are based on feedback obtained through ten interviews with researchers and therapists, as well as an evaluation plan for the final release of CoVoL.

cs.SE↗

arXiv2025

Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions

Introduction: Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method: This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI). We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes. Results: This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.

cs.SE↗

arXiv2025

Not real or too soft? On the challenges of publishing interdisciplinary software engineering research

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

cs.SE↗

arXiv2024

A Socio-Technical Grounded Theory on the Effect of Cognitive Dysfunctions in the Performance of Software Developers with ADHD and Autism

The concept of neurodiversity, encompassing conditions such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), dyslexia, and dyspraxia, challenges traditional views of these neurodevelopmental variations as disorders and instead frames them as natural cognitive differences that contribute to unique ways of thinking and problem-solving. Within the software development industry, known for its emphasis on innovation, there is growing recognition of the value neurodivergent individuals bring to technical teams. Despite this, research on the contributions of neurodivergent individuals in Software Engineering (SE) remains limited. This interdisciplinary Socio-Technical Grounded Theory study addresses this gap by exploring the experiences of neurodivergent software engineers with ASD and ADHD, examining the cognitive and emotional challenges they face in software teams. Based on interviews and a survey with 25 neurodivergent and 5 neurotypical individuals, our theory describes how neurodivergent cognitive dysfunctions affect SE performance, and how the individuals' individual journey and various accommodations can regulate this effect. We conclude our paper with a list of inclusive Agile practices, allowing organizations to better support neurodivergent employees and fully leverage their capabilities.

cs.SE↗

arXiv2024

Exploring Actions, Interactions and Challenges in Software Modelling Tasks: An Empirical Investigation with Students

Background: Software modelling is a creative yet challenging task. Modellers often find themselves lost in the process, from understanding the modelling problem to solving it with proper modelling strategies and modelling tools. Students learning modelling often get overwhelmed with the notations and tools. To teach students systematic modelling, we must investigate students' practical modelling knowledge and the challenges they face while modelling. Aim: We aim to explore students' modelling knowledge and modelling actions. Further, we want to investigate students' challenges while solving a modelling task on specific modelling tools. Method: We conducted an empirical study by observing 16 pairs of students from two universities and countries solving modelling tasks for one hour. Results: We find distinct patterns of modelling of class and sequence diagrams based on individual modelling styles, the tools' interface and modelling knowledge. We observed how modelling tools influence students' modelling styles and how they can be used to foster students' confidence and creativity. Based on these observations, we developed a set of guidelines aimed at enhancing modelling education and helping students acquire practical modelling skills. Conclusions: The guidance for modelling in education needs to be structured and systematic. Our findings reveal that different modelling styles exist, which should be properly studied. It is essential to nurture the creative aspect of a modeller, particularly while they are still students. Therefore, selecting the right tool is important, and students should understand how a tool can influence their modelling style.

cs.SE↗

arXiv2024

Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models

Modern blockchain, such as Ethereum, supports the deployment and execution of so-called smart contracts, autonomous digital programs with significant value of cryptocurrency. Executing smart contracts requires gas costs paid by users, which define the limits of the contract's execution. Logic vulnerabilities in smart contracts can lead to financial losses, and are often the root cause of high-impact cyberattacks. Our objective is threefold: (i) empirically investigate logic vulnerabilities in real-world smart contracts extracted from code changes on GitHub, (ii) introduce Soley, an automated method for detecting logic vulnerabilities in smart contracts, leveraging Large Language Models (LLMs), and (iii) examine mitigation strategies employed by smart contract developers to address these vulnerabilities in real-world scenarios. We obtained smart contracts and related code changes from GitHub. To address the first and third objectives, we qualitatively investigated available logic vulnerabilities using an open coding method. We identified these vulnerabilities and their mitigation strategies. For the second objective, we extracted various logic vulnerabilities, applied preprocessing techniques, and implemented and trained the proposed Soley model. We evaluated Soley along with the performance of various LLMs and compared the results with the state-of-the-art baseline on the task of logic vulnerability detection. From our analysis, we identified nine novel logic vulnerabilities, extending existing taxonomies with these vulnerabilities. Furthermore, we introduced several mitigation strategies extracted from observed developer modifications in real-world scenarios. Our Soley method outperforms existing methods in automatically identifying logic vulnerabilities. Interestingly, the efficacy of LLMs in this task was evident without requiring extensive feature engineering.

cs.ET↗

arXiv2024

PrAIoritize: Automated Early Prediction and Prioritization of Vulnerabilities in Smart Contracts

Context:Smart contracts are prone to numerous security threats due to undisclosed vulnerabilities and code weaknesses. In Ethereum smart contracts, the challenges of timely addressing these code weaknesses highlight the critical need for automated early prediction and prioritization during the code review process. Efficient prioritization is crucial for smart contract security. Objective:Toward this end, our research aims to provide an automated approach, PrAIoritize, for prioritizing and predicting critical code weaknesses in Ethereum smart contracts during the code review process. Method: To do so, we collected smart contract code reviews sourced from Open Source Software (OSS) on GitHub and the Common Vulnerabilities and Exposures (CVE) database. Subsequently, we developed PrAIoritize, an innovative automated prioritization approach. PrAIoritize integrates advanced Large Language Models (LLMs) with sophisticated natural language processing (NLP) techniques. PrAIoritize automates code review labeling by employing a domain-specific lexicon of smart contract weaknesses and their impacts. Following this, feature engineering is conducted for code reviews, and a pre-trained DistilBERT model is utilized for priority classification. Finally, the model is trained and evaluated using code reviews of smart contracts. Results: Our evaluation demonstrates significant improvement over state-of-the-art baselines and commonly used pre-trained models (e.g. T5) for similar classification tasks, with 4.82\%-27.94\% increase in F-measure, precision, and recall. Conclusion: By leveraging PrAIoritize, practitioners can efficiently prioritize smart contract code weaknesses, addressing critical code weaknesses promptly and reducing the time and effort required for manual triage.

cs.SE↗

arXiv2024

Human Factors in Model-Driven Engineering: Future Research Goals and Initiatives for MDE

Purpose: Software modelling and Model-Driven Engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values.

cs.SE↗

arXiv2023

Challenges, Strengths, and Strategies of Software Engineers with ADHD: A Case Study

Neurodiversity describes brain function variation in individuals, including Attention deficit hyperactivity disorder (ADHD) and Autism spectrum disorder. Neurodivergent individuals both experience challenges and exhibit strengths in the workplace. As an important disorder included under the neurodiversity term, an estimated 5.0% to 7.1% of the world population have ADHD. However, existing studies involving ADHD in the workplace are of general nature and do not focus on software engineering (SE) activities. To address this gap, we performed an exploratory qualitative case study on the experiences of people with ADHD working in SE. We find that people with ADHD struggle with several important SE-related activities, e.g., task organisation and estimation, attention to work, relation to others. Furthermore, they experience issues with physical and mental health. In terms of strengths, they exhibit, e.g., increased creative skills, perform well when solving puzzles, and have the capability to think ahead. Our findings align well with existing clinical ADHD research, and have important implications to SE practice.

cs.SE↗

arXiv2023

Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodiversity is common in the general population, with an estimated 5.0% to 7.1% and 7% of the world population being diagnosed with ADHD and dyslexia respectively. Neurodivergent (ND) individuals often experience challenges in specific tasks, such as difficulties in communication or a reduced attention span in comparison to neurotypical (NT) individuals. However, they also exhibit specific strengths, such as high creativity or attention to detail. Therefore, improving the inclusion of ND individuals is desirable for economic, ethical, and for talent reasons. In higher education, struggles of ND students are well-documented. Common issues in this area are a lack of awareness among other students and staff, forms of assessment that are particularly challenging for some students, and a lack of offered accommodations. These factors commonly lead to stress, anxiety, and ultimately a risk of dropping out of the studies. Accommodations for ND students can require substantial effort. However, smaller changes in course material can already have major impact. In this chapter, we summarise the lessons learned from an intervention in four courses in undergraduate computer science programmes at Reykjavik University, Iceland, over a period of two terms. Following accessibility guidelines produced by interest groups for different ND conditions, we created course material in the form of slides and assignments specifically tailored to ND audiences. We focused on small, economical changes that could be replicated by educators with a minimal investment of time. We evaluated the success of our intervention through two surveys, showing an overall positive response among ND students and NT students.

cs.SE↗

arXiv2023

We Do Not Understand What It Says -- Studying Student Perceptions of Software Modelling

Background: Despite the potential benefits of software modelling, developers have shown a considerable reluctance towards its application. There is substantial existing research studying industrial use and technical challenges of modelling. However, there is a lack of detailed empirical work investigating how students perceive modelling. Aim: We investigate the perceptions of students towards modelling in a university environment. Method: We conducted a multiple case study with 5 cases (5 courses from 3 universities) and two units of analysis (student and instructor). We collected data through 21 semi-structured interviews, which we analysed using in-vivo coding and thematic analysis. Results: Students see some benefits of modelling, e.g., using models for planning and communicating within the group. However, several factors negatively influence their understanding of modelling, e.g., assignments with unclear expectations, irregular and insufficient feedback on their models, and lack of experience with the problem domains. Conclusions: Our findings help in understanding better why students struggle with software modelling, and might be reluctant to adopt it later on. This could help to improve education and training in software modelling, both at university and in industry. Specifically, we recommend that educators try to provide feedback beyond syntactical issues, and to consider using problem domains that students are knowledgeable about.

cs.SE↗

arXiv2022

AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated method for mining and classifying Ethereum's smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contracts' vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records.

cs.CR↗