Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants

The predictability of program execution provides attackers a rich source of knowledge who can exploit it to spy or remotely control the program. Moving target defense addresses this issue by constantly switching between many diverse variants of a program, which reduces the certainty that an attacker can have about the program execution. The effectiveness of this approach relies on the availability of a large number of software variants that exhibit different executions. However, current approaches rely on the natural diversity provided by off-the-shelf components, which is very limited. In this paper, we explore the automatic synthesis of large sets of program variants, called sosies. Sosies provide the same expected functionality as the original program, while exhibiting different executions. They are said to be computationally diverse. This work addresses two objectives: comparing different transformations for increasing the likelihood of sosie synthesis (densifying the search space for sosies); demonstrating computation diversity in synthesized sosies. We synthesized 30184 sosies in total, for 9 large, real-world, open source applications. For all these programs we identified one type of program analysis that systematically increases the density of sosies; we measured computation diversity for sosies of 3 programs and found diversity in method calls or data in more than 40% of sosies. This is a step towards controlled massive unpredictability of software

DSpot: Test Amplification for Automatic Assessment of Computational Diversity

Context: Computational diversity, i.e., the presence of a set of programs that all perform compatible services but that exhibit behavioral differences under certain conditions, is essential for fault tolerance and security. Objective: We aim at proposing an approach for automatically assessing the presence of computational diversity. In this work, computationally diverse variants are defined as (i) sharing the same API, (ii) behaving the same according to an input-output based specification (a test-suite) and (iii) exhibiting observable differences when they run outside the specified input space. Method: Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We quantify computational diversity as the dissimilarity between observations on inputs that are outside the specified domain. Results: We run our experiments on 472 variants of 7 classes from open-source, large and thoroughly tested Java classes. Our test amplification multiplies by ten the number of input points in the test suite and is effective at detecting software diversity. Conclusion: The key insights of this study are: the systematic exploration of the observable output space of a class provides new insights about its degree of encapsulation; the behavioral diversity that we observe originates from areas of the code that are characterized by their flexibility (caching, checking, formatting, etc.).Comment: 12 page

The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond

Early experiments with software diversity in the mid 1970's investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault-tolerance, security, software engineering); means (managed or automated diversity) and analytical studies (quantification of diversity and its impact). Our paper contributes to the field of software diversity as the first paper that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity, to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to present. The targeted audience is researchers and practitioners in one of the surveyed fields, who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field

Genetic Improvement of Software (Dagstuhl Seminar 18052)

We document the program and the immediate outcomes of Dagstuhl Seminar 18052 “Genetic Improvement of Software”. The seminar brought together researchers in Genetic Improvement (GI) and related areas of software engineering to investigate what is achievable with current technology and the current impediments to progress and how GI can affect the software development process. Several talks covered the state-of-the-art and work in progress. Seven emergent topics have been identified ranging from the nature of the GI search space through benchmarking and practical applications. The seminar has already resulted in multiple research paper publications. Four by participants of the seminar will be presented at the GI workshop co-located with the top conference in software engineering - ICSE. Several researchers started new collaborations, results of which we hope to see in the near future

Assessing Product Line Derivation Operators Applied to Java Source Code: An Empirical Study

International audienceProduct Derivation is a key activity in Software Product Line Engineering. During this process, derivation operators modify or create core assets (e.g., model elements, source code instructions, components) by adding, removing or substituting them according to a given configuration. The result is a derived product that generally needs to conform to a programming or modeling language. Some operators lead to invalid products when applied to certain assets, some others do not; knowing this in advance can help to better use them, however this is challenging, specially if we consider assets expressed in extensive and complex languages such as Java. In this paper, we empirically answer the following question: which product line operators, applied to which program elements , can synthesize variants of programs that are incorrect , correct or perhaps even conforming to test suites? We implement source code transformations, based on the derivation operators of the Common Variability Language. We automatically synthesize more than 370,000 program variants from a set of 8 real large Java projects (up to 85,000 lines of code), obtaining an extensive panorama of the sanity of the operations

COMPASS: A Community-driven Parallelization Advisor for Sequential Software

The widespread adoption of multicores has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this paper, we describe the research being conducted at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers while they reengineer their code for parallelism. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some similar code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ability to automatically seek, find and synthesize this wisdom into advice that is tailored to the task at hand, i.e., the code the user is considering parallelizing and the environment in which the optimized program is planned to execute. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization — widely, and on diverse hardware and software. By leveraging the "wisdom of crowds" model, which has been conjectured to scale exponentially and which has successfully worked for wikis, COMPASS aims to enable rapid propagation of knowledge about code parallelization in the context of the actual parallelization reengineering, and thus continue to extend the benefits of Moore's law scaling to science and society