Plagiarism is a significant challenge in computer science education. Thus, tool-based approaches are widely used to combat software plagiarism. However, especially due to the recent rise of automated obfuscation via algorithmic or AIbased techniques, these tools face difficulties due to increasingly sophisticated obfuscation techniques. To address this challenge, we present a novel defense mechanism against automated obfuscation attacks. This mechanism iteratively merges matching program subsequences to counteract the effects of the obfuscation. Our approach is language-independent, attack-agnostic, and integrates well into state-of-the-art software plagiarism detectors. The evaluation based on five real-world datasets indicates that our approach not only provides broader resilience against algorithmic and AI-based obfuscation attacks than the state-of-the-art but also improves the detection of fully AI-generated programs.

While software plagiarism detectors have been used for decades, the assumption that evading detection requires programming proficiency is challenged by the emergence of automated plagiarism generators. These generators enable effortless obfuscation attacks, exploiting vulnerabilities in existing detectors by inserting statements to disrupt the matching of related programs. Thus, we present a novel, language-independent defense mechanism that leverages program dependence graphs, rendering such attacks infeasible. We evaluate our approach with multiple real-world datasets and show that it defeats plagiarism generators by offering resilience against automated obfuscation while maintaining a low rate of false positives.

Abstract Modelling is an important activity in software development and it is essential that students learn the relevant skills. Modelling relies on dedicated tools and these can be complex to install, configure, and use—distracting students from learning key modelling concepts and creating accidental complexity for teachers. To address these challenges, we believe that modelling tools specifically aimed at use in teaching are required. Based on discussions at a working session organised at MODELS 2023 and the results from an internationally shared questionnaire, we report on requirements for such modelling tools for teaching. We also present examples of existing modelling tools for teaching and how they address some of the requirements identified.

Context: Software architecture (SA) as research area experienced an increase in empirical research, as identified by Galster and Weyns in 2016 [1]. Empirical research builds a sound foundation for the validity and comparability of the research. A current overview on the evaluation and replicability of SA research objects could help to discuss our empirical standards as a community. However, no such current overview exists.Objective: We aim at assessing the current state of practice of evaluating SA research objects and replication artifact provision in full technical conference papers from 2017 to 2021.Method: We first create a categorization of papers regarding their evaluation and provision of replication artifacts. In a systematic literature review (SLR) with 153 papers we then investigate how SA research objects are evaluated and how artifacts are made available.Results: We found that technical experiments (28%) and case studies (29%) are the most frequently used evaluation methods over all research objects. Functional suitability (46% of evaluated properties) and performance (29%) are the most evaluated properties. 17 papers (11%) provide replication packages and 97 papers (63%) explicitly state threats to validity. 17% of papers reference guidelines for evaluations and 14% of papers reference guidelines for threats to validity.Conclusions: Our results indicate that the generalizability and repeatability of evaluations could be improved to enhance the maturity of the field; although, there are valid reasons for contributions to not publish their data. We derive from our findings a set of four proposals for improving the state of practice in evaluating software architecture research objects. Researchers can use our results to find recommendations on relevant properties to evaluate and evaluation methods to use and to identify reusable evaluation artifacts to compare their novel ideas with other research. Reviewers can use our results to compare the evaluation and replicability of submissions with the state of the practice.

Models are used in software engineering to describe parts of a system that are relevant for the computation of specific analyses, or the provision of specific functionality. Metamodeling languages such as Ecore make it possible to realize analyses and functionality with model-driven technology, such as transformation engines. If models conform to a metamodel that was expressed using Ecore, numerous Eclipse-based tools can be reused to directly analyze, display, or transform models. In many software projects, models are, however, realized with objects of plain-old Java classes rather than an explicit metamodel, so these popular toolscannot be used.In this new ideas paper, we present an Ecoreification approach, which can be used to automatically extract Ecore-conforming metamodels from Java code, and a code generator that combines the benefits of both worlds. The resulting code can be used exactly as before, but it also uses the modeling infrastructure and implements all interfaces for Ecore-based tooling. This way, arbitrary non-standard models can be displayed and modified, for example using graphical Sirius editors, or transformed with well-proven transformation languages, such as QVT-O or ATL.