We propose, BanditRepair, a system that systematically explores and assesses a set of possible runtime patches. The system is grounded on so-called bandit algorithms, that are online machine learning algorithms, designed for constantly balancing exploitation and exploration. BanditRepair's runtime patches are based on modifying the execution state for repairing null dereferences. BanditRepair constantly trades the ratio of automatically handled failures for searching for new runtime patches and vice versa. We evaluate the system with 16 null dereference field bugs, where BanditRepair identifies a total of 8460 different runtime patches, which are composed of 1 up to 8 decisions (execution modifications) taken in a row. We are the first to finely characterize the search space and the outcomes of runtime repair based on execution modification.
@cite_1 presents a language for the specification of data structure invariants. The invariant specification is used to verify and repair the consistency of data structure instances at runtime. The key difference between their work and ours is that is more generic in scope, only requiring a laps model and a laps oracle, which go beyond data structure errors and invariant restoration only.
@cite_1
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Geo-replicated storage systems are at the core of current Internet services. The designers of the replication protocols used by these systems must choose between either supporting low-latency, eventually-consistent operations, or ensuring strong consistency to ease application correctness. We propose an alternative consistency model, Explicit Consistency, that strengthens eventual consistency with a guarantee to preserve specific invariants defined by the applications. Given these application-specific invariants, a system that supports Explicit Consistency identifies which operations would be unsafe under concurrent execution, and allows programmers to select either violation-avoidance or invariant-repair techniques. We show how to achieve the former, while allowing operations to complete locally in the common case, by relying on a reservation system that moves coordination off the critical path of operation execution. The latter, in turn, allows operations to execute without restriction, and restore invariants by applying a repair operation to the database state. We present the design and evaluation of Indigo, a middleware that provides Explicit Consistency on top of a causally-consistent data store. Indigo guarantees strong application invariants while providing similar latency to an eventually-consistent system in the common case. In programming languages with dynamic use of memory, such as Java, knowing that a reference variable x points to an acyclic data structure is valuable for the analysis of termination and resource usage (e.g., execution time or memory consumption). For instance, this information guarantees that the depth of the data structure to which x points is greater than the depth of the data structure pointed to by x.f for any field f of x. This, in turn, allows bounding the number of iterations of a loop which traverses the structure by its depth, which is essential in order to prove the termination or infer the resource usage of the loop. The present paper provides an Abstract-Interpretation-based formalization of a static analysis for inferring acyclicity, which works on the reduced product of two abstract domains: reachability, which models the property that the location pointed to by a variable w can be reached by dereferencing another variable v (in this case, v is said to reach w); and cyclicity, modeling the property that v can point to a cyclic data structure. The analysis is proven to be sound and optimal with respect to the chosen abstraction. Many approaches to software verification are currently semi-automatic: a human must provide key logical insights - e.g., loop invariants, class invariants, and frame axioms that limit the scope of changes that must be analyzed. This paper describes a technique for automatically inferring frame axioms of procedures and loops using static analysis. The technique builds on a pointer analysis that generates limited information about all data structures in the heap. Our technique uses that information to over-approximate a potentially unbounded set of memory locations modified by each procedure loop; this over- approximation is a candidate frame axiom. We have tested this approach on the buffer-overflow benchmarks from ASE 2007. With manually provided specifications and invariants axioms, our tool could verify falsify 226 of the 289 benchmarks. With our automatically inferred frame axioms, the tool could verify falsify 203 of the 289, demonstrating the effectiveness of our approach. Programming languages such as C, C++ and Java bind variables to dynamically-allocated data-structures held in memory. This lets programs build cyclical data at run-time, which complicates termination analysis and garbage collection. It is hence desirable to spot those variables which are only bound to non-cyclical data at run-time. We solve this problem by using abstract interpretation to define the abstract domain NC representing those variables. We relate NC through a Galois insertion to the concrete domain of program states. Hence NC is not redundant. We define a correct abstract denotational semantics over NC, which uses preliminary sharing information between variables to get more precise results. We apply it to a simple example of analysis. We use a Boolean representation for the abstract denotations over NC, which leads to an efficient implementation in terms of binary decision diagrams and to the elegant and efficient use of abstract compilation.