Applying Soft Computing Techniques for Software Project Effort Estimation Modelling

Sharma, Sudhir; Vijayvargiya, Shripal

doi:10.1007/978-981-15-7486-3_21

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…The soft computing techniques of linear regression (LR), SVR, ANN, RF, DT, and bagging methodology were used by Sharma and Vijayvargiya [14] to predict the time and resources required for software projects using the benchmark datasets. It was decided that the results from the RF and choice tree were the most helpful.…”

Section: Related Workmentioning

confidence: 99%

Software Effort Estimation using Machine Learning Technique

Rahman¹,

Roy²,

Ali³

et al. 2023

IJACSA

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Software engineering effort estimation plays a significant role in managing project cost, quality, and time and creating software. Researchers have been paying close attention to software estimation during the past few decades, and a great amount of work has been done utilizing a variety of machinelearning techniques and algorithms. In order to better effectively evaluate predictions, this study recommends various machine learning algorithms for estimating, including k-nearest neighbor regression, support vector regression, and decision trees. These methods are now used by the software development industry for software estimating with the goal of overcoming the limitations of parametric and conventional estimation techniques and advancing projects. Our dataset, which was created by a software company called Edusoft Consulted LTD, was used to assess the effectiveness of the established method. The three commonly used performance evaluation measures, mean absolute error (MAE), mean squared error (MSE), and R square error, represent the base for these. Comparative experimental results demonstrate that decision trees perform better at predicting effort than other techniques.

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Section: Related Workmentioning

confidence: 99%

Software Effort Estimation using Machine Learning Technique

Rahman¹,

Roy²,

Ali³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…The support vector relates to points that are closest to the hyperplane, while the margins correspond to distance between the support vectors. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane [ 35 ]. Using these support vectors, the margins of the classifier are maximized.…”

Section: System Configurationmentioning

confidence: 99%

A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Ahmad

Sidén

Andersson

2021

Sensors

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This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

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“…A broad spectrum of techniques exists in the public domain to assess & estimate the effort required to test software [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]. However, none of the literature cited includes any viable technique suitable to the early stages of commercial engagement.…”

Section: Client-side Issuesmentioning

confidence: 99%

An experimentally verified method to predict & measure all forms of Cyber-Risk

Storti¹

2021

Preprint

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A precise & unambiguous mathematical definition of Cyber-Risk is developed, yielding an experimentally validated solution demonstrating ‘How to Predict & Measure Cyber-Risk’ for any Internet Connected Information System (ICIS) to greater than 98.07% accuracy. Moreover, it is shown that the solution holds for all scales of ICIS, from an Application level to an Enterprise level. In addition, it is shown that Test Effort Estimation (TEE) quantifies Cyber-Confidence, which in turn quantifies Cyber-Risk. Hence, TEE is a Mission Critical Activity (MCA) when formulating Cyber-Risk Management Strategies & may be utilised prior to project commencement, in-flight or post facto as an assessment &/or auditing tool. The TEE Model Construct developed is a statistical based methodology whereby the evaluations/decisions made, result in the contraction or expansion of the ‘z-Score’ associated with an infinite population of database records. The primary advantage of this approach is that very little information is required client-side at the engagement stage in order to produce peer acceptable estimates of the required test effort, & to accurately predict & measure the associated Cyber-Risk. This approach empowers clients & service providers to precisely define whatever level of Cyber-Risk is to be contractually delivered, capable of being absorbed, or prepared to be absorbed by consensus. With the aid of a decision table, estimators are able to articulate & convey to the appropriate authorities, various levels of Cyber-Risk commensurate with the available resources. The TEE Model Construct developed, presents an experimentally verified methodology, cognizant of commercial realities, yielding the following key advantages; (i) it requires minimal inputs, (ii) it has a scientific foundation, (iii) it facilitates operational decision-making, (iv) it quantifies Risk Based Testing (RBT), (v) it is simple, robust, flexible, consistent, reusable & transparent, (vi) it is capable of scaling a projected solution from a known solution, (vii) it embraces Continuous Improvement Processes (CIP’s), (viii) it confines perceptual subjectivity predominantly to three variables & (ix), it commercially exists as an off-the-shelf product. <br>

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Applying Soft Computing Techniques for Software Project Effort Estimation Modelling

Cited by 7 publications

References 30 publications

Software Effort Estimation using Machine Learning Technique

Software Effort Estimation using Machine Learning Technique

A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

An experimentally verified method to predict & measure all forms of Cyber-Risk

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