Project description
Paving the way forward in deep program understanding
In the realm of computer science, achieving automatic, insightful comprehension of programs has been a long-standing challenge. While static program analysis has made strides, the dream of true automatic program understanding persists. The ERC-funded PINDESYM project, led by a renowned researcher in declarative program analysis, aims to revolutionise this landscape. By combining declarative systems with symbolic reasoning and integrating machine learning approaches, PINDESYM seeks to propel program analysis to new heights. The project’s ambitious goal is to create a unified, reusable and extensible framework for deep program understanding. This is a breakthrough poised to transcend current limitations and redefine the future of static program analysis.
Objective
The automatic understanding of programs, in insightful, high-level terms, has long been a dream of computer science. The area of static program analysis has made significant progress in such understanding by algorithmically modeling all possible program behaviors. In this setting, declarative program analysis has recently demonstrated great success in capturing powerful algorithms efficiently and elegantly, in a form that bridges mathematical logic and intuitive human understanding.
The PI’s research has established a world-leading program in declarative program analysis, with multiple independent signs of high recognition. However, the dream of automatic deep program understanding remains elusive: static analysis tools are still reliant on significant human insights and extensive customization for the analysis domain. Is there hope for a giant step forward? The PINDESYM approach posits that two emerging breakthroughs offer excellent promise to take declarative program analysis to a next level, capable of realizing the dream of automatic program understanding. The first is the idea of combining a declarative system (e.g. a Datalog fixpoint engine) and a symbolic reasoning system, such as an SMT solver or algebraic rewrite system. The second is the seamless integration of a machine learning approach, over large amounts of data (from past code bases), in the declarative inference process.
The PINDESYM project will leverage symbolic reasoning and learning approaches to greatly advance
program analysis. The challenge is dual: not only to invent powerful new techniques and algorithms, but also to capture all the diversity in symbolic, value-flow, and learning-based reasoning in a single, unified, reusable, and extensible analysis framework—a true deep program understanding engine, far beyond current approaches.
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
10561 Athina
Greece