Analysis of Software Patches Using Numerical Abstract Interpretation

International audienceWe present a static analysis for software patches. Given two syntactically close versions of a program, our analysis can infer a semantic difference, and prove that both programs compute the same outputs when run on the same inputs. Our method is based on abstract interpretation, and parametric in the choice of an abstract domain. We focus on numeric properties only. Our method is able to deal with unbounded executions of infinite-state programs, reading from infinite input streams. Yet, it is limited to comparing terminating executions, ignoring non terminating ones.We first present a novel concrete collecting semantics, expressing the behaviors of both programs at the same time. Then, we propose an abstraction of infinite input streams able to prove that programs that read from the same stream compute equal output values. We then show how to leverage classic numeric abstract domains, such as polyhedra or octagons, to build an effective static analysis. We also introduce a novel numeric domain to bound differences between the values of the variables in the two programs, which has linear cost, and the right amount of relationality to express useful properties of software patches.We implemented a prototype and experimented on a few small examples from the literature. Our prototype operates on a toy language, and assumes a joint syntactic representation of two versions of a program given, which distinguishes between common and distinctive parts

Putting the Semantics into Semantic Versioning

The long-standing aspiration for software reuse has made astonishing strides in the past few years. Many modern software development ecosystems now come with rich sets of publicly-available components contributed by the community. Downstream developers can leverage these upstream components, boosting their productivity. However, components evolve at their own pace. This imposes obligations on and yields benefits for downstream developers, especially since changes can be breaking, requiring additional downstream work to adapt to. Upgrading too late leaves downstream vulnerable to security issues and missing out on useful improvements; upgrading too early results in excess work. Semantic versioning has been proposed as an elegant mechanism to communicate levels of compatibility, enabling downstream developers to automate dependency upgrades. While it is questionable whether a version number can adequately characterize version compatibility in general, we argue that developers would greatly benefit from tools such as semantic version calculators to help them upgrade safely. The time is now for the research community to develop such tools: large component ecosystems exist and are accessible, component interactions have become observable through automated builds, and recent advances in program analysis make the development of relevant tools feasible. In particular, contracts (both traditional and lightweight) are a promising input to semantic versioning calculators, which can suggest whether an upgrade is likely to be safe.Comment: to be published as Onward! Essays 202

Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi