A complexity measure based on nesting level

Harrison, Warren; Magel, Kenneth

doi:10.1145/947825.947829

Cited by 115 publications

(75 citation statements)

References 2 publications

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“…Potency (strength against humans) can be evaluated with software complexity metrics. Program Length [11], Nesting Complexity [12], and Data Flow Complexity [22] are increased by our obfuscating transformation and we rate its potency level similar to Collberg's transformation "Parallelize Code" (potency level: high). Both methods hide the control flow graph and allow the attacker only local view on small code blocks.…”

Section: Discussionmentioning

confidence: 99%

“…Software complexity metrics (e.g. [6,12,22,11,13,21,19]), which were developed to reduce the complexity of software, can be used to evaluate this rather subjective metric. In contrast to potency that evaluates the strength of the obfuscating transformation against humans, resilience defines how well it withstands an attack of an automatic deobfuscator.…”

Section: Related Workmentioning

confidence: 99%

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Code Obfuscation against Static and Dynamic Reverse Engineering

Schrittwieser

Katzenbeisser

2011

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Abstract. The process of reverse engineering allows attackers to understand the behavior of software and extract proprietary algorithms and data structures (e.g. cryptographic keys) from it. Code obfuscation is frequently employed to mitigate this risk. However, while most of today's obfuscation methods are targeted against static reverse engineering, where the attacker analyzes the code without actually executing it, they are still insecure against dynamic analysis techniques, where the behavior of the software is inspected at runtime. In this paper, we introduce a novel code obfuscation scheme that applies the concept of software diversification to the control flow graph of the software to enhance its complexity. Our approach aims at making dynamic reverse engineering considerably harder as the information an attacker can retrieve from the analysis of a single run of the program with a certain input, is useless for understanding the program behavior on other inputs. Based on a prototype implementation we show that our approach improves resistance against both static disassembling tools and dynamic reverse engineering at a reasonable performance penalty.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Code Obfuscation against Static and Dynamic Reverse Engineering

Schrittwieser

Katzenbeisser

2011

Information Hiding

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“…1) Size based metrics Size is one of the most essential attributes of software systems [6]. It controls the expenditure incurred for the systems both in man-power and budget, for the development and maintenance.…”

Section: Software Complexity Measures: Attributesmentioning

confidence: 99%

“…In a program characteristic that is one of the responsible factors that affect the developer's productivity [8] in program comprehension, maintenance, and testing phase. There are several methods to calculate complexity measures were investigated, e.g., Nesting Level [6], different version of LOC [8] …”

Section: Software Complexity Measures: Attributesmentioning

confidence: 99%

A Review and Analysis of Software Complexity Metrics in Structural Testing

Debbarma¹,

Debbarma²,

Debbarma³

et al. 2013

IJCCE

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Abstract-Software metrics is developed and used by the various software organizations for evaluating and assuring software code quality, operation, and maintenance. Software metrics measure various types of software complexity like size metrics, control flow metrics and data flow metrics. These software complexities must be continuously calculated, followed, and controlled. One of the main objectives of software metrics is that applies to a process and product metrics. It is always considered that high degree of complexity in a module is bad in comparison to a low degree of complexity in a module. Software metrics can be used in different phases of the software development lifecycle. This paper reviews the theory, called "software complexity metrics", and analysis has been done based on static analysis. We try to evaluate and analyze different aspects of software metrics in structural testing which offers of estimating the effort needed for testing.

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“…The first reactions, however, were to redefine the metric in order to accommodate problems in the way the graph represented certain program structures. New variations of the metric quickly emerged where more emphasis was placed on the bounds of ELSE statements[l7], arcs at a node forming "knots"[l8], the flow of both control and data [19] and the nesting depth of statements (e.g., (20)). The most important use of these graph-theoretic measures of complexity has been to explain the number and distribution of errors [21,22].…”

Section: Cyclomatic Complextiymentioning

confidence: 99%

Cyclomatic complexity as a measure of the structure of knowledge-based/expert systems

Moores

1995

Software Quality and Productivity

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The "structure" of a program is often used as an indicator of the likely error-proneness of a system (and thus its quality). This paper reviews the applicability of cyclomatic complexity as a measure of the structure of knowledge-based systems (KBSs). Threshold values are sought above which 80% of error-prone programs are captured. The results obtained suggest that cyclomatic complexity substantially outperforms lines of code as a measure of the error-proneness ofKBSs.

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A complexity measure based on nesting level

Cited by 115 publications

References 2 publications

Code Obfuscation against Static and Dynamic Reverse Engineering

Code Obfuscation against Static and Dynamic Reverse Engineering

A Review and Analysis of Software Complexity Metrics in Structural Testing

Cyclomatic complexity as a measure of the structure of knowledge-based/expert systems

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