Modeling expert effort estimation of software projects

Karna, Hrvoje; Gotovac, S.

doi:10.1109/softcom.2014.7039106

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…Data used for the study refers to the tracking system items and descriptive features of the estimators, as these are the entities used to construct the predictive models. The authors implemented these models before [15], [40], so the selection of predictors was based on their relative importance determined in this, our previous [41] and similar studies [2].…”

Section: Methodsmentioning

confidence: 99%

Data Mining Approach to Effort Modeling On Agile Software Projects

Karna

Gotovac²,

Vickovic³

2020

IJCAI

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Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.Povzetek: V prispevku je predstavljen pristop strojnega rudarjenja za modeliranje agilnih programskih projektov.

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Section: Methodsmentioning

confidence: 99%

Data Mining Approach to Effort Modeling On Agile Software Projects

Karna

Gotovac²,

Vickovic³

2020

IJCAI

Self Cite

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“…An Adaptive Learning Approach to Software Cost Estimation article by Reddy and Raju [4,15] proposes the use of back propagation neural networks as an approach to perform cost estimation for software. An artificial neural network is almost similar to the biological neural networks.…”

Section: Related Workmentioning

confidence: 99%

“…Karna and Gotovac [15] modeled expert effort estimation by developing collected data in a real environment using effort estimation methodology. The article shows the challenges that are faced by the experts when performing estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Modeling Expert Effort Estimation of Software Projects [15] They still face challenges that are related to the nature of producing the estimates, their triggers of estimation errors.…”

Section: Paper Titlementioning

confidence: 99%

“…That to get the accuracy level of each estimation parameters, the most used are measure the average of estimation accuracy of the Mean scale of Relative Error (MRE), where the MRE of each estimate defined as [15]. Also, comparison result among these techniques described in the following comparison section • Able to repeatable estimations.…”

Section: 1accuracy Criteriamentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Software Estimation Techniques

Aljohani¹,

Qureshi²

2017

IJSEA

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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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Modeling expert effort estimation of software projects

Cited by 3 publications

References 21 publications

Data Mining Approach to Effort Modeling On Agile Software Projects

Data Mining Approach to Effort Modeling On Agile Software Projects

Comparative Study of Software Estimation Techniques

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

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