Observations of a software engineering studio:reflecting with the studio framework

Studio-based learning for software engineering is a well-received concept, despite its apparent lack of uptake across institutions worldwide. Studio education affords a variety of highly desirable benefits, and is also popular amongst its students. This paper presents Lancaster University’s software engineering studio, details of its implementation, observations made throughout its first year, evidence of its successes, and reflections against the recently defined studio framework . This paper aims to provide useful information for anyone that is considering utilizing a studio-based approach

Cue Now, Reflect Later: A Study of Delayed Reflection of Diary Events

Diary studies require participants to record entries at the moment of events, but the process often distracts the participants and disrupts the flow of the events. In this work, we explore the notion of delayed reflection for diary studies. Users quickly denote cues of diary events and only reflect on the cues later when they are not busy. To minimize disruptions, we employed a squeeze gesture that is swift and discreet for denoting cues. We investigated the feasibility of delayed reflection and compared it against a conventional digital diary that requires users to reflect immediately at the time of entry. In a weeklong field study, we asked participants to record their daily experiences with both types of diaries. Our results show that users’ preference is context-dependent. Delayed reflection is favored for use in contexts when interruptions are deemed inappropriate (e.g. in meetings or lectures) or when the users are mobile (e.g. walking). In contrast, the users prefer immediate reflection when they are alone, such as during leisure and downtime

The state of practice in model-driven engineering

Despite lively debate over the last decade on the benefits or drawbacks of model-driven engineering (MDE), there have been very few industry-wide studies of MDE in practice. We present a new study, covering a broad range of experiences and ways of applying MDE: we surveyed 450 MDE practitioners and carried out in-depth interviews with 22 more. Findings suggest that MDE may be more widespread than commonly believed, but developers rarely use it to generate whole systems; rather, they apply it to develop key parts of a system often using domain-specific modeling languages developed specifically for the purpose. Our findings also suggest reasons why some efforts to adopt MDE fail and some succeed. As is usually the case in software engineering, adoption largely depends on social and organizational factors, some of which we describe in this paper

Exploring Qualitative Research Using LLMs

The advent of AI driven large language models (LLMs) have stirred discussions about their role in qualitative research. Some view these as tools to enrich human understanding, while others perceive them as threats to the core values of the discipline. This study aimed to compare and contrast the comprehension capabilities of humans and LLMs. We conducted an experiment with small sample of Alexa app reviews, initially classified by a human analyst. LLMs were then asked to classify these reviews and provide the reasoning behind each classification. We compared the results with human classification and reasoning. The research indicated a significant alignment between human and ChatGPT 3.5 classifications in one third of cases, and a slightly lower alignment with GPT4 in over a quarter of cases. The two AI models showed a higher alignment, observed in more than half of the instances. However, a consensus across all three methods was seen only in about one fifth of the classifications. In the comparison of human and LLMs reasoning, it appears that human analysts lean heavily on their individual experiences. As expected, LLMs, on the other hand, base their reasoning on the specific word choices found in app reviews and the functional components of the app itself. Our results highlight the potential for effective human LLM collaboration, suggesting a synergistic rather than competitive relationship. Researchers must continuously evaluate LLMs role in their work, thereby fostering a future where AI and humans jointly enrich qualitative research

Revealing flows in the local economy through visualisations:customers, clicks/cliques and clusters

It is well known by now, that the world has suffered an economic downturn. This has led many governments and organisations to invest resources into researching varying strategies to combat such problem. For some time now, governments have been promoting growth by encouraging local spending; we have witnessed this through ?shop local? campaigns and local currencies. We introduce BARTER a moBile sociAl netwoRking supporTing local Ethical tRading system to tackle this issue, at it?s core an information system that encompasses technology, social media and business analytics are brought together to engage customers, traders and citizens to spend locally by featuring the intrinsic and extrinsic motivations of trading local. After situating BARTER at the heart of the community (with varying traders in and around Lancaster, UK) for some time, this paper is a follow on from a ?BARTER Visualisations? design concept, reporting on the progression and recent developments in the project. Whilst these systems are in place within the community, further research is being conducted to evaluate if revealing and transforming transaction data in a playful and informative manner will help citizens better understand the flow of money in the local economy

Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design

Text-based user-kNN:measuring user similarity based on text reviews

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based userkNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE

An ML Editor based on Proofs-as-Programs

. C Y NTHIA is a novel editor for the functional programming language ML in which each function definition is represented as the proof of a simple specification. Users of C Y NTHIA edit programs by applying sequences of high-level editing commands to existing programs. These commands make changes to the proof representation from which a new program is then extracted. The use of proofs is a sound framework for analysing ML programs and giving useful feedback about errors. Amongst the properties analysed within C Y NTHIA at present is termination. C Y NTHIA has been successfully used in the teaching of ML in two courses at Napier University. 1 Introduction Current programming environments for novice functional programming (FP) are inadequate. This paper describes ways of using mechanised theorem proving to improve the situation, in the context of the language ML [9]. ML is a stronglytyped FP language with type inference [4]. ML incorporates extensive use of pattern match..