Bottom-Up Shape Analysis

In this paper we present a new shape analysis algorithm. The key distinguishing aspect of our algorithm is that it is completely compositional, bottom-up and non-iterative. We present our algorithm as an inference system for computing Hoare triples summarizing heap manipulating programs. Our inference rules are compositional: Hoare triples for a compound statement are computed from the Hoare triples of its component statements. These inference rules are used as the basis for a bottom-up shape analysis of programs. Specifically, we present a logic of iterated separation formula (LISF) which uses the iterated separating conjunct of Reynolds [17] to represent program states. A key ingredient of our inference rules is a strong biabduction operation between two logical formulas. We describe sound strong bi-abduction and satisfiability decision procedures for LISF. We have built a prototype tool that implements these inference rules and have evaluated it on standard shape analysis benchmark programs. Preliminary results show that our tool can generate expressive summaries, which are complete functional specifications in many cases

Automated Specification Inference in a Combined Domain via User-Defined Predicates

Discovering program specifications automatically for heap-manipulating programs is a challenging task due\ud to the complexity of aliasing and mutability of data structures. This task is further complicated by an\ud expressive domain that combines shape, numerical and bag information. In this paper, we propose a compositional analysis framework that would derive the summary for each method in the expressive abstract\ud domain, independently from its callers. We propose a novel abstraction method with a bi-abduction technique in the combined domain to discover pre-/post-conditions that could not be automatically inferred\ud before. The analysis does not only infer memory safety properties, but also finds relationships between pure\ud and shape domains towards full functional correctness of programs. A prototype of the framework has been\ud implemented and initial experiments have shown that our approach can discover interesting properties for\ud non-trivial programs

Program Analysis in A Combined Abstract Domain

Automated verification of heap-manipulating programs is a challenging task due to the complexity of aliasing and mutability of data structures used in these programs. The properties of a number of important data structures do not only relate to one domain, but to combined multiple domains, such as sorted list, priority queues, height-balanced trees and so on. The safety and sometimes efficiency of programs do rely on the properties of those data structures. This thesis focuses on developing a verification system for both functional correctness and memory safety of such programs which involve heap-based data structures. Two automated inference mechanisms are presented for heap-manipulating programs in this thesis. Firstly, an abstract interpretation based approach is proposed to synthesise program invariants in a combined pure and shape domain. Newly designed abstraction, join and widening operators have been defined for the combined domain. Furthermore, a compositional analysis approach is described to discover both pre-/post-conditions of programs with a bi-abduction technique in the combined domain. As results of my thesis, both inference approaches have been implemented and the obtained results validate the feasibility and precision of proposed approaches. The outcomes of the thesis confirm that it is possible and practical to analyse heap-manipulating programs automatically and precisely by using abstract interpretation in a sophisticated combined domain

Local reasoning about the presence of bugs: Incorrectness Separation Logic

There has been a large body of work on local reasoning for proving the absence of bugs, but none for proving their presence. We present a new formal framework for local reasoning about the presence of bugs, building on two complementary foundations: 1) separation logic and 2) incorrectness logic. We explore the theory of this new incorrectness separation logic (ISL), and use it to derive a begin-anywhere, intra-procedural symbolic execution analysis that has no false positives by construction. In so doing, we take a step towards transferring modular, scalable techniques from the world of program verification to bug catching