Estimating software development effort using Bayesian networks

Karna, Hrvoje; Gotovac, S.

doi:10.1109/softcom.2015.7314091

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…It exploits graphical models to represent sets of variables coupled with their conditional dependencies through a directed acyclic graph (DAG) [33]. In other terms it uses Bayesian inference to perform probability computations they aim at modeling conditional dependence while developing software cost estimation which is usually represented by the edges within a directed graph.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…In other terms it uses Bayesian inference to perform probability computations they aim at modeling conditional dependence while developing software cost estimation which is usually represented by the edges within a directed graph. In addition to that, the model uses three main inference tasks: inferring unobserved variables, parameter learning and structure learning [33]. Through developers of software understanding the different relationships that exist in the model, they can efficiently conduct inference on random variables within software [33].…”

Section: Bayesian Networkmentioning

confidence: 99%

“…In addition to that, the model uses three main inference tasks: inferring unobserved variables, parameter learning and structure learning [33]. Through developers of software understanding the different relationships that exist in the model, they can efficiently conduct inference on random variables within software [33]. Each edge in the model corresponds to a conditional dependency while at the same time each node corresponds to a unique random variable.…”

Section: Bayesian Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Software Cost Estimation Techniques

Chirra¹,

Reza²

2019

JSEA

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The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.

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Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

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A Survey on Software Cost Estimation Techniques

Chirra¹,

Reza²

2019

JSEA

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“…Six inputs parameters were used that are a number of floors, floor area, the number of basements, concrete volume, formworks area, and reinforcing steel weight. [13] [5], Proposed an approach for improving effort estimation by using NNs as Bayesian networks (BN). They test major tree's entities in the work items, the estimation process, and estimators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Incorrect, both underestimates or overestimates of the effort required, complicate scheduling, and management result in over budgets and late in the projects. These reasons represent why effort estimation is somewhat known as sort of critical part [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network based Prediction of Software Effort

Hussein¹,

Nassar²,

Naser³

2017

IJCA

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The enormous efforts of software systems and unexpected efforts in the late phases of software development in software engineering field led to using methods to estimate software effort at early stages of software preparing phases. Therefore, the question remains how can develop an estimation method to be more accurate and gives a prediction for future software efforts. This paper presents a proposed method for software effort prediction, to enhance software effort estimation phase. The proposed method utilizes feed-forward neural network in recurrent fashion to make a prediction and adapt to handle with varying software types in software engineering. The proposed method (RFFNN) used to enhance the results of ordinary software effort estimation methods, RFFNN gives more efficient results by making a prediction for future software efforts. General TermsSoftware engineering

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Techniques Based on Data Science for Software Processes: A Systematic Literature Review

Carpio¹,

Angarita

2018

Communications in Computer and Information Science

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Estimating software development effort using Bayesian networks

Cited by 9 publications

References 15 publications

A Survey on Software Cost Estimation Techniques

A Survey on Software Cost Estimation Techniques

Recurrent Neural Network based Prediction of Software Effort

Techniques Based on Data Science for Software Processes: A Systematic Literature Review

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