A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions

By A. Al-Rabadi, M. Perkowski, Martin Zwick. 2004

4,505 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/1e41ed1ac234cba0138329047e16a8a424389e77

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Citations in the corpus

Predicting the location and number of faults in large software systems (2005, background): “Since the cyclomatic number [8] has been found to be very highly correlated with the number of lines of code, it is not surprising that this did not enhance the predictive power of the model.”
In-IDE Code Generation from Natural Language: Promise and Challenges (2021, background): “of source lines of code (SLOC) and McCabe’s cyclomatic complexity (CC), a measure of the number of linearly independent paths through a program’s source code [73]; in real programs, CC depends a lot on the “if”-statements, as well as conditional loops, and whether these are nested.”
Predicting defect-prone software modules using support vector machines (2008, methodology): “The independent variables are 21 static metrics at the module-level including McCabe (McCabe, 1976; McCabe and Butler, 1989), Halstead (basic and derived) (Halstead, 1977), Line Count, and Branch Count.”
Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities (2011, background): “Complex code is difficult to understand, maintain, and test [18].”
Data Mining Static Code Attributes to Learn Defect Predictors (2007, methodology): “Many researchers use static attributes to guide software quality predictions (see [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]).”
Towards identifying software project clusters with regard to defect prediction (2010, background): “Therefore, there is no point in testing the hypothesis.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “geting one branch at a time [1], [19], [23], [33], and (ii)”
Mining Semantic Loop Idioms from Big Code (2016, methodology): “Cyclomatic complexity (McCabe 1976) measures the number of independent paths, and is used as a measure of code complexity; in our sample it is most often around 3 for foreach and for, and around 4 for while; this indicates that developers pack a conditional inside a short loop. do loops’…”
Easy over hard: a case study on deep learning (2017, methodology): “McCabe metrics, Halstead’s effort metrics and CK object-oriented code mertics [13, 26, 31, 45]).”
Persuasive Explanation of Reasoning Inferences on Dietary Data (2019, background): “In particular, seminal work described in [16], which devised a complexity metric known as cyclomatic complexity”
Learning a Metric for Code Readability (2010, methodology): “We use McCabe's Cyclomatic measure [27] as an indicative example of an automated model of code complexity.”
Using Class Imbalance Learning for Software Defect Prediction (2013, methodology): “For describing the attributes of a module, which is usually the smallest unit of functionality, static code metrics defined by McCabe [1] and Halstead [2] have been widely used and commonly accepted by most papers.”