One Metric to Combine Them All: Experimental Comparison of Metric Aggregation Approaches in Software Quality Models

Wolski, Marcin; Walter, Bartosz; Kupiński, Szymon; Promiński, Patryk

doi:10.1109/iwsm-mensura.2016.031

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The diameter of the circle represents the standard deviation of the subcharacteristics: The larger the circle is, the more diverse the subcharacteristics are. Providing a measure of diversity helps in evaluating the quality not only at the Tier 1 level, but also to understand how the underlying subcharacteristics support it.…”

Section: Resultsmentioning

confidence: 99%

Software quality model for a research‐driven organization—An experience report

Wolski

Walter

Kupiński

et al. 2017

J Software Evolu Process

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In the paper, we present a measurement framework for evaluating quality in software products developed within the research and innovation framework project GÉANT. The proposed framework is based on the quality models by Boehm and McCall, but also addresses the presence and point of view of a third stakeholder: an external funding agency (EU), which has started and is temporally financing the project, but aims at making it self-financing in the future. We also provide results of evaluation of 2 projects from the GÉANT ecosystem and one open-source system with this framework. KEYWORDS quality models, software measurement, software process improvement, software quality

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Section: Resultsmentioning

confidence: 99%

Software quality model for a research‐driven organization—An experience report

Wolski

Walter

Kupiński

et al. 2017

J Software Evolu Process

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“…The weight w i of the i th location information entry is, as shown in Equation , the aggregation of five different weights w i 1 , w i 2 , w i 3 , w i 4 and w i 5 . Such an aggregation provides a better reliability and robustness against measurement errors

P ((),,, x, y, z) = \frac{\sum_{i = 1}^{n} w_{i} L_{i} ((),,, x, y, z)}{\sum_{i = 1}^{n} w_{i}},

w_{i} = \frac{α_{1} w_{i 1} + α_{2} w_{i 2} + α_{3} w_{i 3} + α_{4} w_{i 4} + α_{5} w_{i 5}}{α_{1} + α_{2} + α_{3} + α_{4} + α_{5}} .

…”

Section: Aggregate Weighted Localization Algorithmmentioning

confidence: 99%

Weighted localization in mobile wireless networks

Mahjri

Medjiah

Dhraief

et al. 2017

Internet Technology Letters

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Localization of wireless devices is a crucial requirement for many emerging applications such as environmental monitoring, intelligent transportation, home automation, health‐care monitoring, and social networking. In this letter, we propose AWL a new aggregate weighted localization algorithm for mobile wireless networks. The proposed algorithm is distributed and requires low computational and communication overheads enabling its use in resource‐limited devices.

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One Metric to Combine Them All: Experimental Comparison of Metric Aggregation Approaches in Software Quality Models

Cited by 2 publications

References 13 publications

Software quality model for a research‐driven organization—An experience report

Software quality model for a research‐driven organization—An experience report

Weighted localization in mobile wireless networks

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