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CUTE: a concolic unit testing engine for C

Sen, Koushik, Marinov, Darko, Agha, Gul·2005·Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering

Abstract

In unit testing, a program is decomposed into units which are collections of functions. A part of unit can be tested by generating inputs for a single entry function. The entry function may contain pointer arguments, in which case the inputs to the unit are memory graphs. The paper addresses the problem of automating unit testing with memory graphs as inputs. The approach used builds on previous work combining symbolic and concrete execution, and more specifically, using such a combination to generate test inputs to explore all feasible execution paths. The current work develops a method to represent and track constraints that capture the behavior of a symbolic execution of a unit with memory graphs as inputs. Moreover, an efficient constraint solver is proposed to facilitate incremental generation of such test inputs. Finally, CUTE, a tool implementing the method is described together with the results of applying CUTE to real-world examples of C code.

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Research works printed but not linked: 24

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Metadata

Authors
Sen, K., Marinov, D., & Agha, G.
Venue
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Publisher
ACM
Published
2005-09
Pages
263-272
OpenAlex
W2009489720
Cited by
1,651
References
1
FWCI
27.45
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: CUTE: a concolic unit testing engine for C. https://app.cochat.ai/papers/10.1145/1081706.1081750
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Sen, K., Marinov, D., & Agha, G. (2005). CUTE: a concolic unit testing engine for C. Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering. https://doi.org/10.1145/1081706.1081750

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.