"Memory Estimation Model for KLEE Symbolic Execution - 200 C Programs"

"This dataset accompanies the research paper entitled \"A Mathematical Memory Estimation Model for Scalable Dynamic Symbolic Execution\" and provides 200 custom C programs designed for memory consumption analysis in the KLEE symbolic execution engine. The programs are organized into ten folders (p1 to p10), each containing 20 C source files. Each program includes symbolic inputs via klee_make_symbolic and multiple conditional branches that generate exponential path growth, mimicking realistic path explosion scenarios.For each program, four key features are reported: number of feasible paths (C), number of conditional expressions (E), number of symbolic variables (V), and variable occurrence count (O). The dataset also includes actual memory consumption measurements (in MB) obtained from complete path exploration using KLEE 3.1 under controlled experimental conditions (Ubuntu 22.04, Intel Core i5-8365U, 32GB RAM, DFS strategy). The measurements cover a wide range of program complexity, from 2 feasible paths up to 2,097,152 paths, with memory consumption ranging from 0.78 MB to over 13 GB.The dataset serves three main purposes: (1) training and validation of memory estimation models for DSE, (2) benchmarking KLEE\u2019s performance across varying program complexity, and (3) enabling reproducible research on path explosion and resource-aware symbolic execution. All programs are original, independently generated by the author, and released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.The complete dataset is available on IEEE DataPort. A companion Excel file (Program_Features_Memory_Results.xlsx) provides the full feature matrix and experimental results for all 200 programs."