Beyond Tests

Abstract

Automated program repair is an emerging technology that seeks to automatically rectify program errors and vulnerabilities. Repair techniques are driven by a correctness criterion that is often in the form of a test suite. Such test-based repair may produce overfitting patches, where the patches produced fail on tests outside the test suite driving the repair. In this work, we present a repair method that fixes program vulnerabilities without the need for a voluminous test suite. Given a vulnerability as evidenced by an exploit, the technique extracts a constraint representing the vulnerability with the help of sanitizers. The extracted constraint serves as a proof obligation that our synthesized patch should satisfy. The proof obligation is met by propagating the extracted constraint to locations that are deemed to be “suitable” fix locations. An implementation of our approach (E xtract F ix ) on top of the KLEE symbolic execution engine shows its efficacy in fixing a wide range of vulnerabilities taken from the ManyBugs benchmark, real-world CVEs and Google’s OSS-Fuzz framework. We believe that our work presents a way forward for the overfitting problem in program repair by generalizing observable hazards/vulnerabilities (as constraint) from a single failing test or exploit.

Enhanced evolutionary automated program repair by finer‐granularity ingredients and better search algorithms

“…For example, semantics-based repair approaches usually sort the patches by their complexity and prefer simpler ones. 7,15,37 F I G U R E 2 The example of a patch representation.…”

“…Please note that although this objective reduces the possibility of generating complex patches, it meets existing repair practices. For example, semantics‐based repair approaches usually sort the patches by their complexity and prefer simpler ones 7,15,37 …”

“…This field was first proposed by the seminal work of GenProg [2][3][4] and has gained remarkable progress in the last decade. [5][6][7][8][9][10][11][12][13][14][15][16] These approaches generate multiple patches and validate them via a test suite, and the patches that pass all the tests, which are all called test suite adequate patches, are returned to developers. A family of evolutionary program repair, in which GenProg is a typical example, is influential in this field.…”

“…To relieve developers from the heavy burden, researchers proposed Automated Program Repair (APR) approaches to fully automatically patch buggy programs. This field was first proposed by the seminal work of GenProg 2–4 and has gained remarkable progress in the last decade 5–16 . These approaches generate multiple patches and validate them via a test suite, and the patches that pass all the tests, which are all called test suite adequate patches, are returned to developers.…”

Introduction to the Department of Cardiology in Nanjing First Hospital of Nanjing Medical University, China

“…Such vulnerability hunters only help security experts to detect and localize the location of security vulnerabilities. However, it is incredibly time-consuming and labor-intensive for security experts to repair such security vulnerabilities manually due to the strikingly increasing number of detected vulnerabilities and the complexity of modern software systems [14], [15]. For example, previous studies report that the average time for repairing severe vulnerabilities is 256 days [16] and the life spans of 50% of vulnerabilities even exceed 438 days [17].…”