Abstract
Model checking and conformance testing play an important role in software system design and implementation. From the view of integrating model checking and conformance testing into a tightly coupled validation approach, this paper presents a novel approach to detect latent errors in software implementation. The latent errors can be classified into two kinds, one is called as Unnecessary Implementation Trace, and the other is called as Neglected Implementation Trace. The method complements the incompleteness of security properties for software model checking. More accurate models are characterized to leverage the effectiveness of the model-based software verification and testing combined method.
Highlights
In software engineering practices, model-based software development and analysis methods receive extensive attention [1]
To validate the functional accuracy for a software system only applying model checking followed by conformance testing may not detect some latent implementation errors, that is, the unnecessary implementation errors and the neglected implementation errors
Based on the analogy set of key properties, the Latent Implementation Error Detection (LIED) method applies model checking directly into the actual software implementation to check whether some latent implementation errors exist and utilize the counterexamples, which illustrate the exception behavior executions as intuitive guidance to improve the system models and system implementations respectively
Summary
Model checking and conformance testing play an important role in software system design and implementation. From the view of integrating model checking and conformance testing into a tightly coupled validation approach, this paper presents a novel approach to detect latent errors in software implementation. The latent errors can be classified into two kinds, one is called as Unnecessary Implementation Trace, and the other is called as Neglected Implementation Trace. The method complements the incompleteness of security properties for software model checking. More accurate models are characterized to leverage the effectiveness of the model-based software verification and testing combined method
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