Checking-in on Network Functions

When programming network functions, changes within a packet tend to have consequences---side effects which must be accounted for by network programmers or administrators via arbitrary logic and an innate understanding of dependencies. Examples of this include updating checksums when a packet's contents has been modified or adjusting a payload length field of a IPv6 header if another header is added or updated within a packet. While static-typing captures interface specifications and how packet contents should behave, it does not enforce precise invariants around runtime dependencies like the examples above. Instead, during the design phase of network functions, programmers should be given an easier way to specify checks up front, all without having to account for and keep track of these consequences at each and every step during the development cycle. In keeping with this view, we present a unique approach for adding and generating both static checks and dynamic contracts for specifying and checking packet processing operations. We develop our technique within an existing framework called NetBricks and demonstrate how our approach simplifies and checks common dependent packet and header processing logic that other systems take for granted, all without adding much overhead during development.Comment: ANRW 2019 ~ https://irtf.org/anrw/2019/program.htm

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

Combining Static and Dynamic Contract Checking for Curry

Static type systems are usually not sufficient to express all requirements on function calls. Hence, contracts with pre- and postconditions can be used to express more complex constraints on operations. Contracts can be checked at run time to ensure that operations are only invoked with reasonable arguments and return intended results. Although such dynamic contract checking provides more reliable program execution, it requires execution time and could lead to program crashes that might be detected with more advanced methods at compile time. To improve this situation for declarative languages, we present an approach to combine static and dynamic contract checking for the functional logic language Curry. Based on a formal model of contract checking for functional logic programming, we propose an automatic method to verify contracts at compile time. If a contract is successfully verified, dynamic checking of it can be omitted. This method decreases execution time without degrading reliable program execution. In the best case, when all contracts are statically verified, it provides trust in the software since crashes due to contract violations cannot occur during program execution.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

Exact Gap Computation for Code Coverage Metrics in ISO-C

Test generation and test data selection are difficult tasks for model based testing. Tests for a program can be meld to a test suite. A lot of research is done to quantify the quality and improve a test suite. Code coverage metrics estimate the quality of a test suite. This quality is fine, if the code coverage value is high or 100%. Unfortunately it might be impossible to achieve 100% code coverage because of dead code for example. There is a gap between the feasible and theoretical maximal possible code coverage value. Our review of the research indicates, none of current research is concerned with exact gap computation. This paper presents a framework to compute such gaps exactly in an ISO-C compatible semantic and similar languages. We describe an efficient approximation of the gap in all the other cases. Thus, a tester can decide if more tests might be able or necessary to achieve better coverage.Comment: In Proceedings MBT 2012, arXiv:1202.582