Visible to the public Biblio

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Naeem Esfahani, Eric Yuan, Kyle Canavera, Sam Malek.  2016.  Inferring Software Component Interaction Dependencies for Adaptation Support. ACM Transactions on Autonomous and Adaptive Systems (TAAS). 10(4)

A self-managing software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent “models@runtime” approaches usually require an a priori model for a system’s dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system’s behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. In this article, we demonstrate how such an approach can be realized and effectively used to address a variety of adaptation concerns. In particular, we describe the details of one application of this approach for safely applying dynamic changes to a running software system without creating inconsistencies. We also provide an overview of two other applications of the approach, identifying potentially malicious (abnormal) behavior for self-protection, and improving deployment of software components in a distributed setting for performance self-optimization. Finally, we report on our experiments with engineering self-management features in an emergency deployment system using the proposed mining approach.

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Eric Yuan, Naeem Esfahani, Sam Malek.  2014.  A Systematic Survey of Self-Protecting Software Systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section on Best Papers from SEAMS 2012 . 8(4)

Self-protecting software systems are a class of autonomic systems capable of detecting and mitigating security threats at runtime. They are growing in importance, as the stovepipe static methods of securing software systems have been shown to be inadequate for the challenges posed by modern software systems. Self-protection, like other self-* properties, allows the system to adapt to the changing environment through autonomic means without much human intervention, and can thereby be responsive, agile, and cost effective. While existing research has made significant progress towards autonomic and adaptive security, gaps and challenges remain. This article presents a significant extension of our preliminary study in this area. In particular, unlike our preliminary study, here we have followed a systematic literature review process, which has broadened the scope of our study and strengthened the validity of our conclusions. By proposing and applying a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area, we have identified key patterns, trends and challenges in the existing approaches, which reveals a number of opportunities that will shape the focus of future research efforts.

Eric Yuan, Naeem Esfahani, Sam Malek.  2014.  Automated Mining of Software Component Interactions for Self-Adaptation. SEAMS 2014 Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. :27-36.

A self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent “models@runtime” approaches usually require an a priori model for a system’s dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system’s behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.

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Alireza Sadeghi, Naeem Esfahani, Sam Malek.  2014.  Mining the Categorized Software Repositories to Improve the Analysis of Security Vulnerabilities. Proceedings of the 17th International Conference on Fundamental Approaches to Software Engineering . 8411

Security has become the Achilles’ heel of most modern software systems. Techniques ranging from the manual inspection to automated static and dynamic analyses are commonly employed to identify security vulnerabilities prior to the release of the software. However, these techniques are time consuming and cannot keep up with the complexity of ever-growing software repositories (e.g., Google Play and Apple App Store). In this paper, we aim to improve the status quo and increase the efficiency of static analysis by mining relevant information from vulnerabilities found in the categorized software repositories. The approach relies on the fact that many modern software systems are developed using rich application development frameworks (ADF), allowing us to raise the level of abstraction for detecting vulnerabilities and thereby making it possible to classify the types of vulnerabilities that are encountered in a given category of application. We used open-source software repositories comprising more than 7 million lines of code to demonstrate how our approach can improve the efficiency of static analysis, and in turn, vulnerability detection.

Alireza Sadeghi, Naeem Esfahani, Sam Malek.  2017.  Ensuring the Consistency of Adaptation through Inter- and Intra-Component Dependency Analysis. ACM Transactions on Software Engineering and Methodology (TOSEM). 26(1)

Dynamic adaptation should not leave a software system in an inconsistent state, as it could lead to failure. Prior research has used inter-component dependency models of a system to determine a safe interval for the adaptation of its components, where the most important tradeoff is between disruption in the operations of the system and reachability of safe intervals. This article presents Savasana, which automatically analyzes a software system’s code to extract both inter- and intra-component dependencies. In this way, Savasana is able to obtain more fine-grained models compared to previous approaches. Savasana then uses the detailed models to find safe adaptation intervals that cannot be determined using techniques from prior research. This allows Savasana to achieve a better tradeoff between disruption and reachability. The article demonstrates how Savasana infers safe adaptation intervals for components of a software system under various use cases and conditions.