Abstract
Static source code analysis techniques are gaining relevance in automated assessment of programming assignments as they can provide less rigorous evaluation and more comprehensive and formative feedback. These techniques focus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically represented internally as graphs, after parsing and other preprocessing stages. Static automated assessment techniques, therefore, draw inferences from intermediate representations to determine the correctness of a solution and derive feedback. Consequently, achieving the most effective semantic graph representation of source code for the specific task is critical, impacting both techniques' accuracy, outcome, and execution time. This paper aims to provide a thorough comparison of the most widespread semantic graph representations for the automated assessment of programming assignments, including usage examples, facets, and costs for each of these representations. A benchmark has been conducted to assess their cost using the Abstract Syntax Tree (AST) as a baseline. The results demonstrate that the Code Property Graph (CPG) is the most feature -rich representation, but also the largest and most space -consuming (about 33% more than AST).

Abstract
Clustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students' difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool-AsanasCluster-to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

Abstract
Generative AI presents both challenges and opportunities for educators. This paper explores its potential for automating the creation of programming exercises designed for automated assessment. Traditionally, creating these exercises is a time-intensive and error-prone task that involves developing exercise statements, solutions, and test cases. This ongoing research analyzes the capabilities of the OpenAI GPT API to automatically create these components. An experiment using the OpenAI GPT API to automatically create 120 programming exercises produced interesting results, such as the difficulties encountered in generating valid JSON formats and creating matching test cases for solution code. Learning from this experiment, an enhanced feature was developed to assist teachers in creating programming exercises and was integrated into Agni, a virtual learning environment (VLE). Despite the challenges in generating entirely correct programming exercises, this approach shows potential for reducing the time required to create exercises, thus significantly aiding teachers. The evaluation of this approach, comparing the efficiency and usefulness of using the OpenAI GPT API or authoring the exercises oneself, is in progress. © Yannik Bauer, José Paulo Leal, and Ricardo Queirós;

Abstract
Learning how to program is a difficult task. To acquire the re-quired skills, novice programmers must solve a broad range of programming activities, always supported with timely, rich, and accurate feedback. Automated assessment tools play a major role in fulfilling these needs, being a common pres-ence in introductory programming courses. As programming exercises are not easy to produce and those loaded into these tools must adhere to specific format requirements, teachers often opt for reusing them for several years. There-fore, most automated assessment tools, particularly Mooshak, store hundreds of submissions to the same programming ex-ercises, as these need to be kept after automatically pro-cessed for possible subsequent manual revision. Our dataset consists of the submissions to 16 programming exercises in Mooshak proposed in multiple years within the 2003-2020 timespan to undergraduate Computer Science students at the Faculty of Sciences from the University of Porto. In particular, we extract their code property graphs and store them as CSV files. The analysis of this data can enable, for instance, the generation of more concise and personalized feedback based on similar accepted submissions in the past, the identifica-tion of different strategies to solve a problem, the under -standing of a student's thinking process, among many other findings.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.