Recent advancements in Large Language Models (LLMs) like ChatGPT and Copilot have led to their integration into various educational domains, including software development education. Regular use of LLMs in the learning process is still not well-researched; thus, this paper intends to fill this gap. The paper explores the nuanced impact of informal LLM usage on undergraduate students’ learning outcomes in software development education, focusing on React applications. We carefully designed an experiment involving thirty-two participants over ten weeks where we examined unrestricted but not specifically encouraged LLM use and their correlation with student performance. Our results reveal a significant negative correlation between increased LLM reliance for critical thinking-intensive tasks such as code generation and debugging and lower final grades. Furthermore, a downward trend in final grades is observed with increased average LLM use across all tasks. However, the correlation between the use of LLMs for seeking additional explanations and final grades was not as strong, indicating that LLMs may serve better as a supplementary learning tool. These findings highlight the importance of balancing LLM integration with the cultivation of independent problem-solving skills in programming education.

Cross-platform mobile application development has gained significant traction in recent years, driven by the growing demand for efficient, cost-effective solutions that cater to both iOS and Android platforms. This paper presents a state-of-the-art review of cross-platform mobile application development, emphasizing the industry trends, framework popularity, and adoption in the job market. By analyzing developer preferences, community engagement, and market demand, this study provides a comprehensive overview of how cross-platform mobile development frameworks shape the mobile development landscape. The research employs a data-driven methodology, drawing insights from three key categories: Developer Sentiment and Survey Data, Community Engagement and Usage Data, and Market Adoption and Job Market Data. By analyzing these factors, the study identifies the key challenges and emerging trends shaping cross-platform mobile application development. It assesses the most widely used frameworks, comparing their strengths and weaknesses in real-world applications. Furthermore, the research examines the industry adoption patterns and the presence of these frameworks in job market trends. Unlike earlier research, which included now-obsolete platforms like Windows Phone and frameworks such as Xamarin, this study is tailored to the current cross-platform mobile application development market landscape. The conclusions offer actionable insights for developers and researchers, equipping them with the knowledge needed to navigate the evolving cross-platform mobile application development ecosystem effectively.

Recent literature proposes the use of a proactive password checker as method for preventing users from creating easy-to-guess passwords. Markov models can help us create a more effective password checker that would be able to check the probability of a given password to be chosen by an attacker. We investigate the ability of different Markov models to calculate a variety of passwords from different topics, in order to find out whether one Markov model is sufficient for creating a more effective password checker. The results of our study show that multiple models are required in order to be able to do strength calculations for a wide range of passwords. To the best of our knowledge, this is the first password strength study where the effect of the training password datasets on the success of the model is investigated.

This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. To this end, participants were instructed to rely exclusively on LLMs for writing code, based on a given set of specifications, and their queries were categorized into seven types: Error Fixing (EF), Feature Implementation (FI), Code Optimization (CO), Code Understanding (CU), Best Practices (BP), Documentation (DOC), and Concept Clarification (CC). The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. Additionally, students seeking code understanding (CU) and error fixing performed better, even when normalizing for previous coding ability. These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development.

V sodobnem spletnem razvoju je asinhrona komunikacija ključna za zagotavljanje odzivne uporabniške izkušnje. Tradicionalne metode, kot so povratne funkcije in obljube, lahko postanejo kompleksne. Medtem ko sintaksa async/await poenostavi asinhrono programiranje, še vedno ne rešuje vseh težav. Signali predstavljajo sodobno rešitev za te izzive, saj omogočajo modularno in prilagodljivo obvladovanje dogodkov. Signali omogočajo preprosto registracijo in sprožanje dogodkov ter boljšo modularnost v aplikacijah. Prispevek se osredotoča na preučitev signalov v JavaScriptu, njihovih osnovnih konceptov, primerov uporabe in prednosti, kot so zmanjšanje odvisnosti med komponentami, izboljšana asinhrona komunikacija in poenostavljeno upravljanje stanja. Poudarjena je tudi integracija signalov v priljubljena ogrodja JavaScript, kot so React.js, Vue.js in Angular.

V zborniku sedemindvajsete konference OTS 2024 so objavljeni prispevki strokovnjakov s področja informatike, v katerih so predstavljena nova spoznanja in trendi razvoja, vpeljave, prilagajanja ter upravljanja informacijskih rešitev, kot tudi konkretni uspešni pristopi in dobre prakse. Prispevki naslavljajo področja sodobnih arhitekturnih izzivov, klasične, generativne in globoke umetne inteligence, sodobnih spletnih ali mobilnih uporabniških vmesnikov, kot tudi tradicionalnih, brezstrežniških in decentraliziranih zalednih sistemov v oblaku. Tematike prispevkov obsegajo tudi zagotavljanje ustreznega skalabilnega okolja zanje ter avtomatizacijo testiranja, merjenje kakovosti in dostavo s proaktivnim naslavljanjem najpogostejših kibernetskih napadov. Rdečo nit prispevkov predstavljajo podatkovne tehnologije, ki so zastopane v obliki klasičnih podatkovnih baz, podatkovnih jezer ter učinkovitega zbiranja, obdelave in vizualizacije velepodatkov. Prispevki tako še naprej omogočajo boljšo povezanost IT strokovnjakov, informatikov, arhitektov in razvijalcev IT rešitev in storitev, kot tudi akademske sfere in gospodarstva.