Sai Deep Tetali

Compositional may-must program analysis: unleashing the power of alternation

Program analysis tools typically compute two types of information: (1) may information that is true of all program executions and is used to prove the absence of bugs in the program, and (2) must information that is true of some program executions and is used to prove the existence of bugs in the program. In this paper, we propose a new algorithm, dubbed SMASH, which computes both may and must information compositionally . At each procedure boundary, may and must information is represented and stored as may and must summaries, respectively. Those summaries are computed in a demand driven manner and possibly using summaries of the opposite type. We have implemented SMASH using predicate abstraction (as in SLAM) for the may part and using dynamic test generation (as in DART) for the must part. Results of experiments with 69 Microsoft Windows 7 device drivers show that SMASH can significantly outperform may-only, must-only and non-compositional may-must algorithms. Indeed, our empirical results indicate that most complex code fragments in large programs are actually often either easy to prove irrelevant to the specific property of interest using may analysis or easy to traverse using directed testing. The fine-grained coupling and alternation of may (universal) and must (existential) summaries allows SMASH to easily navigate through these code fragments while traditional may-only, must-only or non-compositional may-must algorithms are stuck in their specific analyses.

The Yogi Project: Software Property Checking via Static Analysis and Testing

We present Yogi , a tool that checks properties of C programs by combining static analysis and testing. Yogi implements the Dash algorithm which performs verification by combining directed testing and abstraction. We have engineered Yogi in such a way that it plugs into Microsofts Static Driver Verifier framework. We have used this framework to run Yogi on 69 Windows Vista drivers with 85 properties. We find that the new algorithm enables Yogi to scale much better than Slam , which is the current engine driving Microsofts Static Driver Verifier.

Titian: data provenance support in Spark

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders-of-magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

Proofs from Tests

We present an algorithm DASH to check if a program P satisfies a safety property φ. The unique feature of this algorithm is that it uses only test generation operations, and it refines and maintains a sound program abstraction as a consequence of failed test generation operations. Thus, each iteration of the algorithm is inexpensive, and can be implemented without any global may-alias information. In particular, we introduce a new refinement operator WPα that uses only the alias information obtained by symbolically executing a test to refine abstractions in a sound manner. We present a full exposition of the DASH algorithm and its theoretical properties. We have implemented DASH in a tool called YOGI that plugs into Microsofts Static Driver Verifier framework. We have used this framework to run YOGI on 69 Windows Vista drivers with 85 properties and find that YOGI scales much better than SLAM, the current engine driving Microsofts Static Driver Verifier.

MrCrypt: static analysis for secure cloud computations

In a common use case for cloud computing, clients upload data and computation to servers that are managed by a third-party infrastructure provider. We describe MrCrypt, a system that provides data confidentiality in this setting by executing client computations on encrypted data. MrCrypt statically analyzes a program to identify the set of operations on each input data column, in order to select an appropriate homomorphic encryption scheme for that column, and then transforms the program to operate over encrypted data. The encrypted data and transformed program are uploaded to the server and executed as usual, and the result of the computation is decrypted on the client side. We have implemented MrCrypt for Java and illustrate its practicality on three standard benchmark suites for the Hadoop MapReduce framework. We have also formalized the approach and proven several soundness and security guarantees.

BigDebug: debugging primitives for interactive big data processing in spark

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today’s data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires re-thinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.First, BigDebug’s simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.

Kuai: A Model Checker for Software-defined Networks

In software-defined networking (SDN), a software controller manages a distributed collection of switches by installing and uninstalling packet-forwarding rules in the switches. SDNs allow flexible implementations for expressive and sophisticated network management policies. We consider the problem of verifying that an SDN satisfies a given safety property. We describe Kuai, a distributed enumerative model checker for SDNs. Kuai takes as input a controller implementation written in Murphi, a description of the network topology (switches and connections), and a safety property, and performs a distributed enumerative reachability analysis on a cluster of machines. Kuai uses a set of partial order reduction techniques specific to the SDN domain that help reduce the state space dramatically. In addition, Kuai performs an automatic abstraction to handle unboundedly many packets traversing the network at a given time and unboundedly many control messages between the controller and the switches. We demonstrate the scalability and coverage of Kuai on standard SDN benchmarks. We show that our set of partial order reduction techniques significantly reduces the state spaces of these benchmarks by many orders of magnitude. In addition, Kuai exploits large-scale distribution to quickly search the reduced state space.

Adding data provenance support to Apache Spark

Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g., from log files) and performing trial-and-error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders of magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.