A software development project quality analysis model based on HMM-FNN

Liu, Yuanxu; Chang, Chaowen; Pei-sheng, Han; Guo, Wu

doi:10.1109/icsess.2017.8342900

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…To this SMS, the important ones are related to projects and refer to "the application of specific quality processes," thus "ensuring that the project outputs are conformant with the standards that are applicable to that project," as well as setting out "quality goals for the project" and what "processes and standards are to be used." Yuanxu et al 55 focus on software quality assessment by applying NNs that receive inputs related to diverse product and process factors. Such factors include ease of use, compliance level, software experience, and others.…”

Section: Software Qualitymentioning

confidence: 99%

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Santos

Vieira

Bravo

et al. 2021

J Software Evolu Process

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Deep learning has recently experienced explosive growth in use, largely due to advances in neural networks and the availability of large corpora of domain data. Project management activities generate and handle large volumes of data. Software engineering closely relates to project management, so software engineering projects must be prone to the use of neural networks. We seek to obtain an accurate vision of how neural networks are being used in software engineering projects through a systematic mapping study. We confirm that neural networks have already made their way into these projects; however, we show that their current uses are limited to certain repetitive and legacy tasks. Given uncovered ample room for expansion, we point out a few directions the industry and academy can lean toward to in the next years for taking better advantage of neural networks in software engineering projects and immediately advancing the field. We investigate if, how, and to what extent have neural networks been employed to the advancement of software engineering projects. As such, a systematic mapping study was conducted, which led to the conclusion that even though these algorithms have indeed been employed on several software engineering tasks, this employment so far has been shy, mostly relying on legacy types of neural networks. More modern variants, namely, deep learning algorithms, are slowly gaining momentum and should be the trend going forward.

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Section: Software Qualitymentioning

confidence: 99%

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Santos

Vieira

Bravo

et al. 2021

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

Software quality models: Exploratory review

Pinedo,

Valles-Coral,

Navarro-Cabrera

et al. 2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The emerging industrialization of software promotes the continuous development of new evaluation models that adapt to the product's characteristics and the organizations' needs. OBJECTIVES: This article aims to identify the software quality models proposed between 2016 and 2020. METHODS: We conducted an exploratory systematic review in the SciELO, IEEE Xplore, and Scopus databases, resulting in 14 research papers proposing new models for assessing software quality. RESULTS: 79% of the research papers were extracted from IEEE Xplore, and 50% of them were authored by individuals affiliated with European institutions. We presented various software quality models focused on assessing both product quality and usability for specific purposes. Additionally, we reported that the proposed models are based on common software quality metrics standards such as CMMI, ISO/IEC 9126, and others. CONCLUSION: The review serves to update the state of the art regarding the software quality models proposed in recent years, which will be valuable for researchers and software developers when seeking evaluation models that align with their specific needs.

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A software development project quality analysis model based on HMM-FNN

Cited by 2 publications

References 2 publications

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Software quality models: Exploratory review

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