A quality attribute like confidentiality is critical to trustworthy software but unfortunately, very challenging to ensure. This is because modern software systems are complex and interconnected. Architecture-based confidentiality analysis enables the early detection of violations, helping to mitigate risks before deployment. However, uncertainty in software systems and their environments complicates precise and comprehensive architectural analysis. Additionally, the complexity of software models and the exponential growth of uncertainty scenarios pose significant challenges for automated mitigation, often leaving software architects to resolve confidentiality violations manually, a process that is both time-intensive and error-prone. In this paper, we extend our machine-learning-based approach to mitigate confidentiality violations. Specifically, we introduce a novel mitigation strategy inspired by TCP Congestion Control, as well as a strategy that capitalizes on clustering techniques to dynamically adjust batch sizes. Our evaluation on three real-world software architectures demonstrates that our extended approach can mitigate confidentiality violations while outperforming the state-of-the-art. Whereas previously the upper limit was 60 times runtime reduction, now we achieve 2298 times reduction, with the median being an elevenfold reduction. Our statistical analysis confirms that the added TCP-inspired strategy is significantly cheaper than the state-of-the-art baseline (Friedman test p = 0.025 and Nemenyi post hoc test p = 0.039 ), while also having a strong practical impact (Kendall’s W = 0.721 ). This extended work deepens our understanding of the nature of uncertainty and also of the techniques optimally suited to mitigating the violations caused by uncertainties. It takes us one step closer to designing trustworthier systems.

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.

Today’s software systems are neither built nor operated in isolation and have to adapt to their environment. Uncertainty in the software and its context is inherently unavoidable and should be actively analyzed and managed already at design time. This includes analyzing the impact of uncertainty on a system’s quality properties, which quickly becomes critical, e.g., regarding confidentiality. When not handled comprehensively, confidentiality violations can occur due to uncertainty that void previous analysis results. There exist many approaches to classify and handle uncertainty. However, without locating the impact of uncertainty, precise mitigation is often impossible. In this paper, we present an uncertainty impact analysis that shows potential confidentiality violations induced by different uncertainty types like structural, behavioral, or environmental uncertainty. This is achieved by combining software-architectural and data flow-based propagation of uncertainty. Our tool-supported approach is a first step towards predicting the impact of uncertainty without laborious modeling and testing of what-if scenarios. The case study-based evaluation shows that our impact analysis accurately predicts confidentiality violations with a high F1-score of 0.94 while reducing the effort of manual analysis by 82%.

Background: Taxonomies are crucial for the development of a research field, as they play a major role in structuring a complex body of knowledge and help to classify processes, approaches, and solutions. While there is an increasing interest in taxonomies in the software engineering (SE) research field, we observe that SE taxonomies are rarely evaluated. Aim: To raise awareness and provide operational guidance on how to evaluate a taxonomy, this paper presents a three step evaluation method evaluating its structure, applicability, and purpose. Method: To show the feasibility and applicability of our approach, we provide a running example and additionally illustrate our approach to a practical case study in SE research. Results and Conclusion: Our method with operational guidance enables SE researchers to systematically evaluate and improve the quality of their taxonomies and support reviewers to systematically assess a taxonomy’s quality.

The high complexity, connectivity, and data exchange of modern software systems make it crucial to consider confidentiality early. An often used mechanism to ensure confidentiality is access control. When the system is modeled during design time, access control can already be analyzed. This enables early identification of confidentiality violations and the ability to analyze the impact of what-if scenarios. However, due to the abstract view of the design time model and the ambiguity in the early stages of development, uncertainties exist in the system environment. These uncertainties can have a direct effect on the validity of access control attributes in use, which might result in compromised confidentiality.To handle such known uncertainty, we present a notion of confidence in the context of design time access control. We define confidence as a composition of known uncertainties in the environment of the system, which influence the validity of access control attributes. We extend an existing modeling and analysis approach for design time access control with our notion of confidence. For evaluation, we apply the notion of confidence to multiple real-world case studies and discuss the resulting benefits for different stages of system development. We also analyze the expressiveness of the extended approach in defining confidentiality constraints and measure the accuracy in identifying confidentiality violations. Our results show that using the notion of confidence increases expressiveness while being able to accurately identify access control violations.

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.