허기홍 교수

Prevalent software engineering practices such as code reuse and the "one-size-fits-all" methodology have contributed to significant and widespread increases in the size and complexity of software. The resulting software bloat has led to decreased performance and increased security vulnerabilities. We propose a system called Chisel to enable programmers to effectively customize and debloat programs. Chisel takes as input a program to be debloated and a high-level specification of its desired func

In this article we present a general method for achieving global static analyzers that are precise, sound, yet also scalable. Our method generalizes the sparse analysis techniques on top of the abstract interpretation framework to support relational as well as non-relational semantics properties for C-like languages. We first use the abstract interpretation framework to have a global static analyzer whose scalability is unattended. Upon this underlying sound static analyzer, we add our generaliz

A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently

We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis's precision, and to use this information to find out when and where the main analysis should turn on or off its context-sensitivity. We formalize th

We present a machine-learning-based technique for selectively applying unsoundness in static analysis. Existing bug-finding static analyzers are unsound in order to be precise and scalable in practice. However, they are uniformly unsound and hence at the risk of missing a large amount of real bugs. By being sound, we can improve the detectability of the analyzer but it often suffers from a large number of false alarms. Our approach aims to strike a balance between these two approaches by selecti

Program analyses necessarily make approximations that often lead them to report true alarms interspersed with many false alarms. We propose a new approach to leverage user feedback to guide program analyses towards true alarms and away from false alarms. Our approach associates each alarm with a confidence value by performing Bayesian inference on a probabilistic model derived from the analysis rules. In each iteration, the user inspects the alarm with the highest confidence and labels its groun

The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning. Existing approaches either involve solving computationally difficult combinatorial problems, or performing parameter estimation in complex statistical models. In this paper, we present Difflog, a technique to extend the logic programming language Datalog to the continuous setting. By attaching real-valued weights to individual rules of a Datalog prog

A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently

Programs often evolve by continuously integrating changes from multiple programmers. The effective adoption of program analysis tools in this continuous integration setting is hindered by the need to only report alarms relevant to a particular program change. We present a probabilistic framework, Drake, to apply program analyses to continuously evolving programs. Drake is applicable to a broad range of analyses that are based on deductive reasoning. The key insight underlying Drake is to compute

We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis's precision, and to use this information to find out when and where the main analysis should turn on or off its context-sensitivity. We formalize th

We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been developed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the analysis designers, but they do not remove it completely; they still leave the nontrivial task of designing so called features to the hands of the

Similar software vulnerabilities recur because developers reuse existing vulnerable code, or make similar mistakes when implementing the same logic. Recently, various analysis techniques have been proposed to find syntactically recurring vulnerabilities via code reuse. However, limited attention has been devoted to semantically recurring ones that share the same vulnerable behavior in different code structures. In this paper, we present a general analysis framework, called TRACER, for detecting

In this article, we present a general method for achieving global static analyzers that are precise and sound, yet also scalable. Our method, on top of the abstract interpretation framework, is a general sparse analysis technique that supports relational as well as nonrelational semantics properties for various programming languages. Analysis designers first use the abstract interpretation framework to have a global and correct static analyzer whose scalability is unattended. Upon this underlyin

We present a new technique for developing a resource-aware program analysis. Such an analysis is aware of constraints on available physical resources, such as memory size, tracks its resource use, and adjusts its behaviors during fixpoint computation in order to meet the constraint and achieve high precision. Our resource-aware analysis adjusts behaviors by coarsening program abstraction, which usually makes the analysis consume less memory and time until completion. It does so multiple times du