Hybrid Concolic Testing

By Rupak Majumdar, Koushik Sen. International Conference on Software Engineering, 2007

443 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/969d2dad771225710ebd5f552308834e414a906f

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Citations in the corpus

Symbolic execution for software testing: three decades later (2013, unknown): “Another successful approach is to interleave symbolic exploration with random testing [29].”
QSYM : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing (2018, background): “The concept of hybrid fuzzing is first proposed by Majumdar and Sen [6].”
KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs (2008, unknown): “Similarly to the search heuristics presented in Section 3, search strategies proposed in the past include Best First Search [16], Gener-ational Search [27], and Hybrid Concolic Testing [34].”
Unleashing Mayhem on Binary Code (2012, methodology): “The hybrid symbolic execution technique we present in this paper is completely different from hybrid concolic testing [19], which interleaves random testing with concolic execution to achieve better code coverage.”
Dynodroid: an input generation system for Android apps (2013, background): “Indeed, the benefits of combining random and symbolic execution have been shown in other domains [20].”
Symbolic execution for software testing in practice: preliminary assessment (2011, methodology): “Recent extensions have tried to address this challenge by using heuristics to guide path exploration [13,29], interleaving symbolic execution with random testing [40], caching function summaries for later use by higher-level functions [26] or eliminating redundant paths by analyzing the values read ”
Automated Whitebox Fuzz Testing (2008, background): “The limitations of depth-ﬁrst search regarding code coverage are well known (e.g., [21]) and are due to the search being too localized.”
A Survey of Symbolic Execution Techniques (2016, background): “symbolic executor can be augmented with fuzzing to reach deeper states in the exploration more quickly and efficiently. Two notable embodiments of this idea are represented by hybrid concolic testing [73] and Driller [101]. BranchPredication. This is a strategy for mitigating misprediction penalties”
Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “To resolve this, we would need to combine existing GUI test generation techniques with symbolic execution [61] or search-based testing techniques [48, 49], in the sense that hybrid concolic testing [72] combines random testing and concolic execution [33].”
An orchestrated survey of methodologies for automated software test case generation (2013, background): “Finally, other techniques (Chipounov et al., 2011; Godefroid et al., 2008b; Majumdar and Sen, 2007; Pasareanu et al., 2008) in this class use specific path exploration strategies that enable generation of test data that cover deep internal parts of a program, which are difficult to cover otherwise.”
Heuristics for Scalable Dynamic Test Generation (2008, result): “This performance is close to that reported in [19] on Vim 5.”
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation (2023, unknown): “, concolic testing [30, 47]) and implementation (e.”
Differential symbolic execution (2008, background): “In [44], Majumdar and Sen extend CUTE to augment concolic testing with random testing to create hybrid concolic testing.”
Automated concolic testing of smartphone apps (2012, background): “, [1, 4, 12– 14, 17, 19, 20, 29]), the problem is still intractable for realworld software.”
VUzzer: Application-aware Evolutionary Fuzzing (2017, methodology): “In this direction, the use of symbolic and concolic execution has shown promising results [33], [47].”