Existing research has shown that vocal and non-vocal human cues correlate with human trust and distrust behaviours, suggesting their potential to measure human trust in robots in real-time. However, there is a lack of research in Human-Robot Interaction that integrates vocal and non-vocal cues into a comprehensive model to measure trust. This paper aims to estimate human trust in robots by examining vocal and non-vocal cues differences between trust and distrust states across multiple sessions of collaborative game-based HRI with 40 participants. Our analysis revealed that vocal and non-vocal human cues can indeed predict trust in HRI, with certain facial expressions, facial movements, and pitch being significant factors. Random Forest classifier achieved the highest accuracy (84 %) in classifying trust states, with key features such as facial expressions (fear, angry), facial blendshapes (cheekSquintRight, jawRight), and vocal characteristics (Duration, Harmonicity std) being the most predictive of trust. These findings demonstrate the importance of combining vocal and non-vocal cues for accurate trust measurement and highlight the potential for real-time trust assessment in robotic systems.

Several techniques have been proposed to (partially) automate code review. Early support consisted in recommending the most suited reviewer for a given change or in prioritizing the review tasks. With the advent of deep learning in software engineering, the level of automation has been pushed to new heights, with approaches able to provide feedback on source code in natural language as a human reviewer would do. Also, recent work documented open source projects adopting Large Language Models (LLMs) as co-reviewers. Although the research in this field is very active, little is known about the actual impact of including automatically generated code reviews in the code review process. While there are many aspects worth investigating (e.g., is knowledge transfer between developers affected?), in this work we focus on three of them: (i) review quality, i.e., the reviewer's ability to identify issues in the code; (ii) review cost, i.e., the time spent reviewing the code; and (iii) reviewer's confidence, i.e., how confident is the reviewer about the provided feedback. We run a controlled experiment with 29 professional developers who reviewed different programs with/without the support of an automatically generated code review. During the experiment we monitored the reviewers' activities, for over 50 hours of recorded code reviews. We show that reviewers consider valid most of the issues automatically identified by the LLM and that the availability of an automated review as a starting point strongly influences their behavior: Reviewers tend to focus on the code locations indicated by the LLM rather than searching for additional issues in other parts of the code. The reviewers who started from an automated review identified a higher number of low-severity issues while, however, not identifying more high-severity issues as compared to a completely manual process. Finally, the automated support did not result in saved time and did not increase the reviewers' confidence.

The rise in the number and diversity of available software development video tutorials has enhanced digital learning for developers but also introduced challenges in locating relevant content efficiently. Existing video search methods, including keyword-based approaches and tools like CodeTube and TechTube, rely primarily on retrieval algorithms such as BM25, which fail to capture the semantic nuances and user intentions behind search queries. To address these limitations, we introduce ISM, an approach that uses SBERT to generate semantically rich vectors from video tutorial transcripts to improve the search for programming video tutorials. By segmenting transcripts and implementing a re-ranking process, ISM effectively preserves context and enhances the relevance of search results. Additionally, ISM generates informative video summaries using GPT-4, allowing developers to quickly assess the relevance of video content. To evaluate our approach, we first performed a quantitative study comparing ISM with the baseline TechTube. The results revealed that ISM performs better in both video retrieval and fragment identification, achieving a Hit@ 5 score of 0.95 and an average F1 score of 0.70 compared to the baseline’s 0.58 and 0.52, respectively. We also performed a user study, which revealed that users strongly preferred the semantic matching capabilities and AI-generated summaries of our approach. This work advances the state-of-the-art in programming video tutorial search and summarization by offering more nuanced and useraligned retrieval and summarization mechanisms.

The landscape of software developer learning re-sources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatG PT presents a new learning paradigm. While existing research explores the potential of LLMs in software development and education, their impact on developers' learning and solution-seeking behavior remains unexplored. To address this gap, we conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed. This study investigates how AI chatbots like ChatGPT have influenced developers' learning preferences when acquiring new skills, ex-ploring technologies, and resolving programming issues. Through quantitative and qualitative analysis, we explore whether AI tools supplement or replace traditional learning resources such as video tutorials, written tutorials, and Q&A forums. Our findings reveal a nuanced view: while video tutorials continue to be highly preferred for their comprehensive coverage, a significant number of respondents view AI chatbots as potential replacements for written tutorials, underscoring a shift towards more interactive and personalized learning experiences. Additionally, AI chatbots are increasingly considered valuable supplements to video tutorials, indicating their growing role in the developers' learning resources. These insights offer valuable directions for educators and the software development community by shedding light on the evolving preferences toward learning resources in the era of ChatGPT.

Accurate automatic code extraction from tutorial videos is crucial for software developers seeking to reuse the code contained in these videos. Current methods using optical character recognition (OCR) often yield inaccurate results due to code complexity and variations in screencast formats. To address this issue, we introduce CodeT5-OCRfix, an approach that leverages the pre-trained code-aware large language model CodeT5 to enhance code extraction accuracy by post-processing OCRed code. We first collect a large and diverse dataset of source code screenshots captured from more than 10K Java projects from GitHub. We then apply the most widely used OCR engine for the task of code extraction from videos, Tesseract, on these screenshots and collect the OCRed code along with the ground truth code extracted from the Java files. We built a training dataset of more than 585K pairs of OCRed and ground truth code pairs, which we then used to fine-tune CodeT5, obtaining our model CodeT5-OCRfix. An empirical evaluation on both screenshots and screencast frames shows that CodeT5-OCRfix outperforms baseline code extraction models and is also more time-efficient. Our approach therefore improves the state-of-the-art in code extraction techniques from screencasts and images.

Mobile apps are one of the most widely used types of software systems in existence today and more programmers and students learn how to develop them everyday. One of the most popular resources for learning mobile programming are videos hosted on social platforms such as YouTube. While useful, this type of resource has also its limitations, especially when developers are looking for user interface (UI) designs for mobile applications, since these are hard to search for and locate in videos. We propose UIScreens, a web-based analysis and search engine that analyzes the visual contents of mobile programming video tutorials, then identifies and extracts the UI screens displayed in the videos. Our tool offers features such as searching for UI screens in videos, displaying an overview of the UI screens identified in a video under each search result, and navigating to the part of a video where a particular UI screen is being displayed and discussed. In a user study, participants agreed that UIScreens is usable and useful to quickly skim through videos, while the UI screens it extracts can help developers further determine the relevance of videos to a search topic.