An Evolution of Software Metrics

Chhillar, Rajender Singh; Gahlot, Sonal

doi:10.1145/3133264.3133297

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Measuring Productivity in Agile Software Development Process: A Scoping Study (2015) [28] ID-2. An Evolution of Software Metrics: A Review (2017) [29] ID-3. Software Product Size Measurement Methods: A Systematic Mapping Study (2014) [30] ID-4.…”

Section: Selection Of Studiesmentioning

confidence: 99%

“…Token Count: "These symbols are called tokens. The basic measures are: n1 = count of unique operators n2 = count of unique operands N1 = count of total occurrences of operators N2 = count of total occurrence of operands In terms of the total tokens used, the size of the program can be expressed as N = N1 + N2" [29] Product Estimation Evaluation ---…”

Section: Id-2 [29]mentioning

confidence: 99%

“…McCabe's Cyclomatic Metric [29] Product Estimation Evaluation ---OBJECT -ORIENTED METRICS Chidamber and Kemerer's Metrics Suite [29]: -Weighted Methods per Class (WMC) [29] -Response for a Class (RFC) [29] -Lack of Cohesion of Methods (LCOM) [29] -Coupling between Object Classes (CBO) [29] -Depth of Inheritance Tree (DIT) [29] -Number of Children (NOC) [29] Product Evaluation ---…”

Section: Software Science Metricsmentioning

confidence: 99%

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Measures related to social and human factors that influence productivity in software development teams

Machuca-Villegas

Gasca‐Hurtado

Muñoz

2021

IJISPM

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Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team's performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts.

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Section: Selection Of Studiesmentioning

confidence: 99%

Section: Id-2 [29]mentioning

confidence: 99%

Section: Software Science Metricsmentioning

confidence: 99%

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Measures related to social and human factors that influence productivity in software development teams

Machuca-Villegas

Gasca‐Hurtado

Muñoz

2021

IJISPM

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“…is metric is hence not defined on a single version of the software project, but it is tailored to analyze the evolution of the source code. e assumption between the usage of Chidamber and Kemerer DIT, depth of inheritance tree [5,14,26,43,44], [20,21,27,32], [23,28,29,39], [35,47] 15 13 Chidamber and Kemerer LCOM, lack of cohesion in methods [5,14,26,43,44], [20,27,28,32], [23,36,39,46,47] 14 12 Chidamber and Kemerer NOC, number of children [5,14,26,43,44], [20,27,32,39], [23,29,35,47] 13 11 Chidamber and Kemerer RFC, response for class [5,15,19,26,…”

Section: (Iii) Change (Number Of Lines Changed In the Class)mentioning

confidence: 99%

“…WMC, weighted method per class, defined in the same way as McCabe's WMC (weighted method count, described below) but applied to a class, i.e., it gives the complexity of that particular class by [20,23,28,29,35,36,39,47,50] 18 16 Chidamber and Kemerer DIT, depth of inheritance tree [5,14,26,43], S13, [20,21,27,32], [23,28,29,35,39,47] 15 13 Chidamber and Kemerer LCOM, lack of cohesion in methods [5,14,26,43], S13, [20,27,28,32], [23,36,39,46,47] 14 12…”

mentioning

confidence: 99%

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Ardito

Coppola

Barbato³

et al. 2020

Scientific Programming

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Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

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Optimizing test case prioritization through ranked NSGA-2 for enhanced fault sensitivity analysis

Garg,

Shekhar

2024

Innovations Syst Softw Eng

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An Evolution of Software Metrics

Cited by 9 publications

References 7 publications

Measures related to social and human factors that influence productivity in software development teams

Measures related to social and human factors that influence productivity in software development teams

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Optimizing test case prioritization through ranked NSGA-2 for enhanced fault sensitivity analysis

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