In Software Reengineering, one of the central artifacts is the source code of the legacy system in question. In fact, in most cases it is the only definitive artifact, because over the time the code has diverged from the original architecture and design documents. The first task of any reengineering project is to gather an understanding of the system's architecture. Therefore, a common approach is to use parsers to translate the source code into a model conforming to the abstract syntax of the programming language the system is implemented in which can then be subject to querying. Despite querying, transformations can be used to generate more abstract views on the system's architecture. This transformation case deals with the creation of a state machine model out of a Java syntax graph. It is derived from a task that originates from a real reengineering project.
In this paper, we describe and present the first dataset of source code plagiarism specifically aimed at contest plagiarism. The dataset contains 251 pairs of plagiarized solutions of competitive programming tasks in Java, as well as 660 non-plagiarized ones, however, the described approach can be used to extend the dataset in the future. Importantly, each pair comes in two versions: (a) "raw" and (b) with participants' repeated template code removed, allowing for evaluating tools in different settings. We used the collected dataset to compare the available source code plagiarism detection too…
Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
University of Bucharest Faculty of Mathematics and Computer Science（布加勒斯大学数学与计算机科学学院）；Softbinator Technologies（Softbinator技术公司）；Delft University of Technology（代尔夫特理工大学）
本文提出利用生成式 AI 自动生成编程竞赛高质量测试用例的 NLP 方法，并在 OJI、Kilonova.ro 和 IIOT 数据集上验证其可发现隐藏错误、减轻教师负担。
Competitive programming remains a very popular activity that combines both software engineering and education. In order to prepare and to practice, contestants use extensive archives of problems from past contents available on various competitive programming platforms. One way to make this process more effective is to provide an automatic tag system for the tasks. Prior works do that by either using the tasks' problem statements or the code of their solutions. In this study, we investigate which information source is more valuable for tag prediction. To answer that question, we compare existin…
Large language models such as Codex, have shown the capability to produce code for many programming tasks. However, the success rate of existing models is low, especially for complex programming tasks. One of the reasons is that language models lack awareness of program semantics, resulting in incorrect programs, or even programs which do not compile. In this paper, we systematically study whether automated program repair (APR) techniques can fix the incorrect solutions produced by language models in LeetCode contests. The goal is to study whether APR techniques can enhance reliability in the …
University of Toronto（多伦多大学）；University of Victoria（维多利亚大学）
AI-supported programming has arrived, as shown by the introduction and successes of large language models for code, such as Copilot/Codex (Github/OpenAI) and AlphaCode (DeepMind). Above human average performance on programming challenges is now possible. However, software engineering is much more than solving programming contests. Moving beyond code completion to AI-supported software engineering will require an AI system that can, among other things, understand how to avoid code smells, to follow language idioms, and eventually (maybe!) propose rational software designs. In this study, we exp…
Max Planck Institute for Informatics Saarland Campus（马克斯·普朗克信息研究所萨尔兰州分校）；Computer Science Saarland University（萨尔兰州大学计算机科学系）；Max Planck Institute for Software Systems Saarland Campus（马克斯·普朗克软件系统研究所萨尔兰州分校）
