1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result;

AUTOSTUB: Genetic Programming-Based Stub Creation for Symbolic Execution

2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s);

Felix Mächtle
Universität zu Lübeck
Ratzeburger Allee 160, 23562 Lübeck, Germany
f.maechtle@uni-luebeck.de
+49 451 3101 6618

Nils Loose
Universität zu Lübeck
Ratzeburger Allee 160, 23562 Lübeck, Germany
n.loose@uni-luebeck.de
+49 451 3101 6605

Jan-Niclas Serr
Universität zu Lübeck
Ratzeburger Allee 160, 23562 Lübeck, Germany
j.serr@uni-luebeck.de
+49 451 3101 6633

Jonas Sander
Universität zu Lübeck
Ratzeburger Allee 160, 23562 Lübeck, Germany
j.sander@uni-luebeck.de
+49 451 3101 6617

Thomas Eisenbarth
Universität zu Lübeck
Ratzeburger Allee 160, 23562 Lübeck, Germany
thomas.eisenbarth@uni-luebeck.de
+49 451 3101 6600



3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition);

Felix Mächtle

4. the abstract of the paper(s);

Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. 
Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs.
In this work, we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programming. 
When the symbolic executor encounters an external function, AutoStub generates training data by executing the function on randomly generated inputs and collecting the outputs. Genetic Programming then derives expressions that approximate the behavior of the function, serving as symbolic stubs. These automatically generated stubs allow the symbolic executor to continue the analysis without manual intervention, enabling the exploration of program paths that were previously intractable.
We demonstrate that AutoStub can automatically approximate external functions with over 90% accuracy for 55% of the functions evaluated, and can infer language-specific behaviors that reveal edge cases crucial for software testing.


5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies;

B, D, G

6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission);


- (B) External and native functions have long been recognized as major obstacles to symbolic execution. Previous automatic solutions relied on one of three approaches: (i) heavyweight, SMT-based, second-order synthesis; (ii) framework-specific documentation; or (iii) handwritten templates. AutoStub derives missing models online during dynamic symbolic execution, enabling the exploration of branches that could not otherwise be analyzed.
- (D) The artifact produced by the evolutionary run is a library containing 273 automatically synthesized symbolic stubs for Java's standard types and math library. They can be used immediately to eliminate the need for engine developers to manually create models for the same methods. Making these “ready-to-link” stubs publicly available would be a valuable standalone software publication
- (G) For decades, the "external function problem" has been cited as a fundamental obstacle to symbolic execution. Once the analyzed code calls an unmodeled library or native method, exploration stops. AutoStub is the first approach to remove this blocker for multiple of real Java standard library methods without human intervention. It turns previously unreachable path space into analyzable territory. In doing so, AutoStub tackles a long-standing, widely recognized difficulty in program analysis.

7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable);

Felix Mächtle, Nils Loose, Jan-Niclas Serr, Jonas Sander, and Thomas Eisenbarth, "AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution", in Proceedings of the 18th ACM/IEEE International Workshop on Search-Based and Fuzz Testing, SBFT 2025, 2025.

8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors;

100% to Felix Mächtle

9. a statement stating why the authors expect that their entry would be the "best"

AutoStub represents a transformative leap, taking evolutionary computation out of experimental environments and embedding it directly into the daily workflows of software security researchers and tool developers. Unlike incremental improvements or theoretical innovations, AutoStub has a tangible, immediate impact. It replaces tedious, expert-driven manual modeling with automated, evolutionary stub synthesis. This streamlines symbolic execution, an essential yet previously limited method in software security, thereby elevating its practical applicability.
Notably, AutoStub has already produced verified, ready-to-use symbolic stubs for parts of the Java standard library. This directly addresses one of the most persistent and challenging obstacles in symbolic execution: external functions. These automatically generated stubs integrate seamlessly into existing symbolic execution engines, unlocking higher path coverage for previously unsolvable benchmarks. This significant achievement is underscored by the development of a new, comprehensive benchmark dataset consisting of 2,730 Java code samples designed specifically to evaluate the symbolic execution of external functions.
By successfully applying genetic programming to solve a well-documented real-world challenge, AutoStub exemplifies how evolutionary computation can improve complex software analysis workflows. Given its immediate practical applicability, demonstrated effectiveness, and potential for adoption in software security, we firmly believe that AutoStub is the most impactful and competitive entry this year.


10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc.

GP (genetic programming)

11. The date of publication of each paper.  If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement.

In press