Viktor Vafeiadis, head of the MPI-SWS Software Analysis and Verification group, has been awarded an ERC Consolidator Grant. Over the next five years, his project "PERSIST: A Semantic Foundation for Persistent Programming" will receive almost 2 million euros, which will allow the group to develop rigorous formal foundations for programs interacting with non-volatile memory. Read more about the PERSIST project below.

One of the other recipients of an ERC Consolidator Grant this year is an MPI alumnus: Neel Krishnaswami was an MPI-SWS postdoc with Derek Dreyer, and he is currently a faculty member at Cambridge.

ERC grants are the most prestigious and the most competitive European-level awards for ground-breaking scientific investigations. This year, less than 14% of all ERC Consolidator Grant applicants across all scientific disciplines received the award, with only 15 awardees in Computer Science across all of Europe! The ERC Consolidator Grant offers funding for researchers with 7 to 12 years of experience after achieving a PhD. You can find more information about ERC Consolidator Grants awarded this year at https://erc.europa.eu/news/CoG-recipients-2020.

The European Research Council (ERC) is a pan-European funding body that supports cutting-edge research. It offers funding for groundbreaking research projects of the highest scientific quality across Europe, across all research areas. Talented researchers from all over the world can receive funding for excellent research in Europe.

The PERSIST Project

Non-volatile memory (NVM) is an emerging technology that provides orders of magnitude faster access to persistent storage (which preserves its contents after a crash or a power failure) than hard disks.  As such, it is expected to radically change how modern applications manage storage, moving away from traditional block-structured file systems to in-memory persistent data structures.

The problem with NVM, however, is that its programming model is standing on very shaky foundations. The persistency semantics of the mainstream architectures is unclear and full of counterintuitive behaviours, which makes writing correct NVM programs a very challenging task.

The project's goal is to develop a solid mathematical basis for determining the semantics of NVM programs and for reasoning about their correctness. More specifically, the plan is to produce:

• Formal persistency models for mainstream hardware architectures,


• Formal persistency models for mainstream programming languages,


• Firmly-grounded higher-level abstractions to ease persistent programming, and


• Effective testing and verification techniques for persistent programs (e.g., program logics and model checking).

Filip Niksic's thesis on "Combinatorial Constructions for Effective Testing" has won the John C. Reynolds Doctoral Dissertation Award for 2020. This is an annual award given by ACM SIGPLAN for a doctoral dissertation in the field of programming languages. Filip was advised by MPI-SWS faculty member Rupak Majumdar.

The award citation reads as follows: Soundness is at the core of most programming language verification techniques. On the other hand, random testing is one of the most commonly used techniques for analyzing software. Developing a theory of soundness for random testing is therefore a very important goal, but very few results existed before this thesis.Randomized techniques are seldom used in (sound) program analyses, which means that addressing the problem required the development of new ways to approaching it. Filip Niksic's thesis is among the first to apply deep techniques from randomized algorithms and combinatorics to the problem of understanding and explaining the effectiveness of random testing. Moreover, the theory helps with the design of new random testing approaches. The thesis addresses a hard problem, brining in novel theory from outside programming languages, and proving hard theorems. As scientists, when we see a phenomenon that we cannot immediately explain (in this case, the effectiveness of random testing), we should try to build a scientific explanation. For some problems, including random testing, it is unclear that one can actually formulate a precise theory, because the "real world" is extremely messy. The fact that Filip Niksic is able to formulate such problems precisely and prove nontrivial theorems about them is surprising and opens the door to a new field.

Anne-Kathrin Schmuck, a postdoctoral fellow in the Rigorous Software Engineering group, was accepted to the Emmy Noether Programme of the German Science Foundation (DFG). This grant programme is the most prestigious programme for early career researchers from the DFG. It provides funding for an independent research group for a period of six years.

Anne-Kathrin's group will be hosted at MPI-SWS in Kaiserslautern and will develop automated modular synthesis techniques for reliable Cyber-Physical System (CPS) design. Her work draws inspiration from both Control Theory and Computer Science and centers around Reactive Synthesis, Supervisory Control Theory and Abstraction-Based Controller Design.

MPI-SWS doctoral student Michael Sammler was awarded a 2020 Google PhD Fellowship (in Systems and Networking) to fund his doctoral research on RefinedC, a Coq-based framework for automating the foundational verification of systems code using refinement and ownership types. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. Michael, who is advised by Deepak Garg and Derek Dreyer, was one of only 53 recipients worldwide in 2020.

Link: https://research.google/outreach/phd-fellowship/

Researchers from the Max Planck Institute for Software Systems (MPI-SWS), the Max Planck Institute for Informatics (MPI-INF), and the Max Planck Institute for Security and Privacy (MPI-SP) have coauthored 17 papers at the colocated LICS 2020 and ICALP 2020, two of the top conferences in theoretical computer science. LICS is the premier conference on logic in computer science and ICALP is the flagship conference of the European Association for Theoretical Computer Science.

MPI-SWS papers:

1. Invariants for Continuous Linear Dynamical Systems. Shaull Almagor, Edon Kelmendi, Joël Ouaknine and James Worrell. ICALP 2020, Track B. [ Video | Paper]


2. The complexity of bounded context switching with dynamic thread creation. Pascal Baumann, Rupak Majumdar, Ramanathan Thinniyam Srinivasan and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]


3. Extensions of ω-Regular Languages. Mikołaj Bojańczyk, Edon Kelmendi, Rafał Stefański and Georg Zetzsche. LICS 2020. [ Video | Paper ]


4. Rational subsets of Baumslag-Solitar groups. Michaël Cadilhac, Dmitry Chistikov and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]


5. On polynomial recursive sequences. Michaël Cadilhac, Filip Mazowiecki, Charles Paperman, Michał Pilipczuk and Géraud Sénizergues. ICALP 2020, Track B. [ Video | Paper ]


6. An Approach to Regular Separability in Vector Addition Systems. Wojciech Czerwiński and Georg Zetzsche. LICS 2020. [ Video | Paper ]


7. The complexity of knapsack problems in wreath products. Michael Figelius, Moses Ganardi, Markus Lohrey and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]


8. The Complexity of Verifying Loop-free Programs as Differentially Private. Marco Gaboardi, Kobbi Nissim and David Purser. ICALP 2020, Track B. [ Video | Paper ]


9. On Decidability of Time-bounded Reachability in CTMDPs. Rupak Majumdar, Mahmoud Salamati and Sadegh Soudjani. ICALP 2020, Track B. [ Video | Paper ]

MPI-INF papers:

1. Scheduling Lower Bounds via AND Subset Sum. Amir Abboud, Karl Bringmann, Danny Hermelin and Dvir Shabtay. ICALP 2020, Track A.  [ Video | Paper ]


2. Faster Minimization of Tardy Processing Time on a Single Machine. Karl Bringmann, Nick Fischer, Danny Hermelin, Dvir Shabtay and Philip Wellnitz. ICALP 2020, Track A. [ Video | Paper ]


3. Hitting Long Directed Cycles is Fixed-Parameter Tractable. Alexander Göke, Dániel Marx and Matthias Mnich. ICALP 2020, Track A. [ Video | Paper ]


4. A (2 + ε)-Factor Approximation Algorithm for Split Vertex Deletion. Daniel Lokshtanov, Pranabendu Misra, Fahad Panolan, Geevarghese Philip and Saket Saurabh. ICALP 2020, Track A. [ Video | Paper ]


5. Hypergraph Isomorphism for Groups with Restricted Composition Factors. Daniel Neuen. ICALP 2020, Track A. [ Video | Paper ]


6. Deterministic Sparse Fourier Transform with an l∞ Guarante. Yi Li and Vasileios Nakos. ICALP 2020, Track A. [ Video | Paper ]

MPI-SP papers:

1. Deciding Differential Privacy for Programs with Finite Inputs and Outputs. Gilles Barthe, Rohit Chadha, Vishal Jagannath, A. Prasad Sistla and Mahesh Viswanathan. LICS 2020. [ Video | Paper ]


2. Universal equivalence and majority on probabilistic programs over finite fields. Charlie Jacomme, Steve Kremer and Gilles Barthe. LICS 2020. [ Video | Paper ]

Due to the impressive advances in Machine Learning and the unlimited availability of data, neural networks are rapidly becoming prevalent in our everyday lives, for instance by assisting in image-classification or decision-making tasks. As a result, there is growing concern regarding the reliability of neural networks in performing these tasks. In particular, it could be disastrous if an autonomous vehicle misclassifies a street sign, or if a recidivism-risk algorithm, which predicts whether a criminal is likely to re-offend, is unfair with respect to race.

In the Practical Formal Methods group at MPI-SWS, we have recently focused on applying techniques from Software Engineering, including static analysis and test generation, to validate and verify properties of neural networks, such as robustness and fairness. In the following, we give a brief overview of three research directions we have been pursuing in this setting.

Blackbox Fuzzing of Neural Networks

By now, it is well known that even very subtle perturbations of a correctly classified image, such as a street sign, could cause a neural network to classify the new image differently. Such perturbed images are referred to as adversarial inputs and pose a critical threat to important applications of Machine Learning, like autonomous driving.

In our group, we recently developed DeepSearch [1], a blackbox-fuzzing technique that generates adversarial inputs for image-classification neural networks. Starting from a correctly classified image, DeepSearch strategically mutates its pixels such that the resulting image is more likely to be adversarial. By using spatial regularities of images, DeepSearch is able to generate adversarial inputs, while only querying the neural network very few times, which entails increased performance of our technique. Moreover, through a refinement step, DeepSearch further reduces the already subtle pixel perturbations of an adversarial input.

Adversarial-Input Detection for Neural Networks

To protect neural networks against adversarial inputs, we have developed RAID [2], a runtime-monitoring technique for detecting whether an input to a neural network is adversarial. Our technique consists of training a secondary classifier to identify differences in neuron activation values between correctly classified and adversarial inputs. RAID is effective in detecting adversarial inputs across a wide range of adversaries even when it is completely unaware of the type of adversary. In addition, we show that there is a simple extension to RAID that allows it to detect adversarial inputs even when these are generated by an adversary that has access to our detection mechanism.

Fairness Certification of Neural Networks

Several studies have recently raised concerns about the fairness of neural networks. To list a few examples, commercial recidivism-risk and health-care systems have been found to be racially biased. There is also empirical evidence of gender bias in image searches, for instance when searching for “CEO”. And facial-recognition systems, which are increasingly used in law enforcement, have been found biased with respect to both gender and race. Consequently, it is critical that we design tools and techniques for certifying fairness of neural networks or characterizing their bias.

We make an important step toward meeting these needs by designing the LIBRA static-analysis framework [3] for certifying causal fairness of neural networks used for classification of tabular data. In particular, given input features considered sensitive to bias, a neural network is causally fair if its output classification is not affected by different values of the sensitive features. On a high level, our approach combines a forward and a backward static analysis. The forward pass aims to divide the input space into independent partitions such that the backward pass is able to effectively determine fairness of each partition. For the partitions where certification succeeds, LIBRA provides definite (in contrast to probabilistic) fairness guarantees; otherwise, it describes the input space for which bias occurs. We have designed this approach to be sound and configurable with respect to scalability and precision, thus enabling pay-as-you-go fairness certification.

References

[1] Fuyuan Zhang, Sankalan Pal Chowdhury and Maria Christakis. DeepSearch: Simple and Effective Blackbox Fuzzing of Deep Neural Networks. CoRR abs/1910.06296, 2019.

[2] Hasan Ferit Eniser, Maria Christakis and Valentin Wüstholz. RAID: Randomized Adversarial-Input Detection for Neural Networks. CoRR abs/2002.02776, 2020.

[3] Caterina Urban, Maria Christakis, Valentin Wüstholz and Fuyuan Zhang. Perfectly Parallel Fairness Certification of Neural Networks. CoRR abs/1912.02499, 2019.

Software systems have become ubiquitous in our modern world and, consequently, so have bugs and glitches. While many software failures are harmless and often merely annoying, some can have catastrophic consequences. Just imagine the dire results of an autonomous car failing to stop at a red traffic light or a plane's control system becoming unresponsive during takeoff or landing.

In our research, we address these problems and develop intelligent tools that help engineers to build safe and reliable hardware, software, and cyber-physical systems. To this end, we employ a unique and promising strategy, which has recently also regained major attention in the artificial intelligence community: combining inductive techniques from the area of machine learning and deductive techniques from the area of mathematical logic (e.g., see the recent Dagstuhl seminar on "Logic and Learning", which was co-organized by one of our group members). Specifically, our research revolves around three topics, to which the remainder of this article is devoted: verification, synthesis, and formal specification languages.

Verification

Verification is an umbrella term referring to tools and techniques that formally prove that a given system satisfies its specification. In the context of software, a popular approach is deductive verification. The idea is easy to describe: first, the given program is augmented with annotations (typically loop invariants, pre-/post-conditions of method calls, and shape properties of data structures), which capture the developer's intent and facilitate the deductive reasoning in a later step; second, the program, together with its annotations, is translated into formulas in a suitable logic, called verification conditions; third, the verification conditions are checked for validity using automated theorem provers. Thanks to brilliant computer scientists, such as Edsger Dijkstra and Tony Hoare, as well as recent advances in constraint solving, the latter two steps can be (almost) entirely automated. However, the first step still remains a manual, error-prone task that requires significant training, experience, and ingenuity. In fact, this is one of the main obstacles preventing a widespread adaptation of formal verification in practice.

To also automate the challenging first step, we have developed a novel approach, called ICE learning [1], which intertwines inductive and deductive reasoning. The key idea is to pit a (deductive) program verifier against an (inductive) learning algorithm, whose goal is to infer suitable annotations from test-runs of the program and failed verification attempts. The actual learning proceeds in rounds. In each round, the learning algorithm proposes candidate annotations based on the data it has gathered so far. The program verifier then tries to prove the program correct using the proposed annotations. If the verification fails, the verifier reports data back to the learning algorithm explaining why the verification has failed. Based on this new information, the learning algorithm refines its conjecture and proceeds to the next round. The loop stops once the annotations are sufficient to prove the program correct.

ICE learning has proven to be a very powerful approach that allows fully automatic verification of a wide variety of programs, ranging from recursive and concurrent programs over numeric data types [1], to algorithms manipulating dynamically allocated data structures [2], to industry-size GPU kernels [3]. In addition, the principles underlying ICE learning can be lifted to other challenging verification tasks, such as the verification of parameterized systems [4] as well as—in ongoing research—to the verification of cyber-physical and AI-based systems. You might want to try a demo immediately in your browser.

Synthesis

Synthesis goes beyond verification and could be considered the holy grail of computer science. In contrast to checking whether a hand-crafted program meets its specification, the dream is to fully automatically generate software (or a circuit for that matter) from specifications in a correct-by-construction manner.

Although this dream is unrealistic in its whole generality, there exist various application domains in which automated synthesis techniques have been applied with great success. In our own research, for instance, we have developed techniques for synthesizing safety controllers for reactive, cyber-physical systems that have to interact with a complex–and perhaps only partially known–environment [5, 6]. Moreover, we have proposed a general framework for generating loop-free code from input-output examples and specifications written in first-order logic [7]. Similar to ICE learning, these methods combine inductive and deductive reasoning, thereby unveiling and exploiting synergies of modern machine learning algorithms and highly-optimized symbolic reasoning engines.

Formal Specification Languages

Both verification and synthesis rely on the ability to write correct formal specifications, which have to precisely capture the engineer’s intuitive understanding of the system in question. In practice, however, formalizing the requirements of a system is notoriously difficult, and it is well known that the use of standard formalisms such as temporal logics requires a level of sophistication that many users might never develop.

We have recently started a new research project to combat this serious obstacle. Its main objective is to design algorithms that learn formal specifications in interaction with human engineers. As a first step towards this goal, we have developed a learning algorithm for the specification language “Linear Temporal Logic (LTL)”, which is the de facto standard in many verification and synthesis applications. You might think of this algorithm as a recommender system for formal specifications: the human engineer provides examples of the desired and undesired behavior of the system in question, while the recommender generates a series of LTL specifications that are consistent with the given examples; the engineer can then either chose one of the generated specifications or provide additional examples and rerun the recommender.

In ongoing research, we are extending our learning algorithm to a wide range of other specification languages, including Computational Tree Logic, Signal Temporal Logic, and the Property Specification Language. Moreover, we are developing feedback mechanisms that allow for a tighter integration of the human engineer into the loop. Again, you can try our technology immediately in your browser.

References

[1] D’Souza, Deepak; Ezudheen, P.; Garg, Pranav; Madhusudan, P.; Neider, Daniel: Horn-ICE Learning for Synthesizing Invariants and Contracts. In: Proceedings of the ACM on Programming Languages (PACMPL), volume 2 issue OOPSLA, pages 131:1–131:25. ACM, 2018.

[2] Neider, Daniel; Madhusudan, P.; Garg, Pranav; Saha, Shambwaditya; Park, Daejun: Invariant Synthesis for Incomplete Verification Engines. In: 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018), volume 10805 of Lecture Notes in Computer Science, pages 232–250. Springer, 2018

[3] Neider, Daniel; Saha, Shambwaditya; Garg, Pranav; Madhusudan, P.: Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants. In: 26th International Static Analysis Symposium (SAS 2019), volume 11822 of Lecture Notes in Computer Science, pages 323–346. Springer, 2019

[4] Neider, Daniel; Jansen, Nils: Regular Model Checking Using Solver Technologies and Automata Learning. In: 5th International NASA Formal Method Symposium (NFM 2013), volume 7871 of Lecture Notes in Computer Science, pages 16–31. Springer, 2013

[5] Neider, Daniel; Topcu, Ufuk: An Automaton Learning Approach to Solving Safety Games over Infinite Graphs. In: 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2016), volume 9636 of Lecture Notes in Computer Science, pages 204–221. Springer, 2016

[6] Neider, Daniel; Markgraf, Oliver: Learning-based Synthesis of Safety Controllers. In: 2019 International Conference on Formal Methods in Computer Aided Design (FMCAD 2019), pages 120–128. IEEE, 2019

[7] Neider, Daniel; Saha, Shambwaditya; Madhusudan, P.: Synthesizing Piece-wise Functions by Learning Classifiers. In: 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2016), volume 9636 of Lecture Notes in Computer Science, pages 186–203. Springer, 2016

[8] Neider, Daniel; Gavran, Ivan: Learning Linear Temporal Properties. In: 2018 International Conference on Formal Methods in Computer Aided Design (FMCAD 2018), pages 148–157. IEEE, 2018