Software Metrics in Static Program Analysis

Vogelsang, Andreas; Fehnker, Ansgar; Huuck, Ralf; Reif, Wolfgang

doi:10.1007/978-3-642-16901-4_32

Cited by 9 publications

(4 citation statements)

References 10 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, for any P ∈ Prog, we have that P ∈ γ(A 2 (P )) implies γ(A 1 (P )) ⊆ γ(A 2 (P )) and P ∈ γ(A 1 (P )) implies γ(A 2 (P )) ⊆ γ(A 1 (P )), so that A 1 = γ A 2 . Example 4.2 Software metrics static analysers [35] deal with nonsemantic program properties, such as the domain in Example 3.2 (1). Bounded model checking [4,34] handles program properties such as those encoded by the domains of Example 3.2 (2)-(3).…”

Section: Program Analysers and Verifiersmentioning

confidence: 99%

Program Analysis is Harder than Verification: A Computability Perspective

Cousot

Giacobazzi

Ranzato

2018

EasyChair Preprints

View full text Add to dashboard Cite

We study from a computability perspective static program analysis, namely detecting sound program assertions, and verification, namely sound checking of program assertions. We first provide a general computability model for domains of program assertions and corresponding program analysers and verifiers. Next, we formalize and prove an instantiation of Rice's Theorem for static program analysis and verification. Then, within this general model, we provide and show a precise statement of the popular belief that program analysis is a harder problem than program verification: we prove that for finite domains of program assertions, program analysis and verification are equivalent problems, while for infinite domains, program analysis is strictly harder than verification.

show abstract

Section: Program Analysers and Verifiersmentioning

confidence: 99%

Program Analysis is Harder than Verification: A Computability Perspective

Cousot

Giacobazzi

Ranzato

2018

EasyChair Preprints

View full text Add to dashboard Cite

show abstract

“…Example 4.2. Software metrics static analysers [35] deal with nonsemantic program properties, such as the domain in Example 3.2 (1). Bounded model checking [4,34] handles program properties such as those encoded by the domains of Example 3.2 (2)-(3).…”

Section: Definition 41 (Program Analyser) Given An Abstract Domain mentioning

confidence: 99%

Program Analysis Is Harder Than Verification: A Computability Perspective

Cousot

Giacobazzi

Ranzato

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We study from a computability perspective static program analysis, namely detecting sound program assertions, and verification, namely sound checking of program assertions. We first design a general computability model for domains of program assertions and corresponding program analysers and verifiers. Next, we formalize and prove an instantiation of Rice's theorem for static program analysis and verification. Then, within this general model, we provide and show a precise statement of the popular belief that program analysis is a harder problem than program verification: we prove that for finite domains of program assertions, program analysis and verification are equivalent problems, while for infinite domains, program analysis is strictly harder than verification.

show abstract

“…A software metric measures or quantifies a software characteristic such as number of classes or lines of code or the number of operations, etc. They help software developers and managers to track the status of software specification or implementation [13]. Metrics for OO software have been a major research topic for more than two decades.…”

Section: A Software Metricsmentioning

confidence: 99%

A Comparative Study on the Effect of Multiple Inheritance Mechanism in Java, C++, and Python on Complexity and Reusability of Code

Albalooshi¹,

Mahmood²

2017

ijacsa

View full text Add to dashboard Cite

Two of the fundamental uses of generalization in object-oriented software development are the reusability of code and better structuring of the description of objects. Multiple inheritance is one of the important features of object-oriented methodologies which enables developers to combine concepts and increase the reusability of the resulting software. However, multiple inheritance is implemented differently in commonly used programming languages. In this paper, we use Chidamber and Kemerer (CK) metrics to study the complexity and reusability of multiple inheritance as implemented in Python, Java, and C++. The analysis of results suggests that out of the three languages investigated Python and C++ offer better reusability of software when using multiple inheritance, whereas Java has major deficiencies when implementing multiple inheritance resulting in poor structure of objects.

show abstract

Software Metrics in Static Program Analysis

Cited by 9 publications

References 10 publications

Program Analysis is Harder than Verification: A Computability Perspective

Program Analysis is Harder than Verification: A Computability Perspective

Program Analysis Is Harder Than Verification: A Computability Perspective

A Comparative Study on the Effect of Multiple Inheritance Mechanism in Java, C++, and Python on Complexity and Reusability of Code

Contact Info

Product

Resources

About