Empirical Analysis of Software Effort Preprocessing Techniques Based on Machine Learning

doi:10.22266/ijies2021.1231.49

Cited by 2 publications

(5 citation statements)

References 43 publications

(72 reference statements)

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“…• Naive Bayes (NB): It is a popular classification algorithm that is commonly used in SDP. It is particularly well-suited for this task because it is simple, efficient, and performs well even with relatively small datasets [6]. It's important to note that while NB is effective for many classification tasks, it may not capture complex dependencies between features.…”

Section: Software Defect Predictionsmentioning

confidence: 99%

“…Several researches works utilizing ML has proposed for SDP [3,4,6,8,14], and also demonstrated success in terms of accuracy, reliability, and performance improvements, there appears to be a lack of clear evidence for predicting the FSD in software developments.…”

Section: Software Defect Predictionsmentioning

confidence: 99%

“…It is useful for providing quantitative estimates of defect proneness [3]. Classification mechanisms [4][5][6] are commonly employed in these methods to categorize instances as either defective or non-defective based on various attributes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

2024

IJIES

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Software has become an essential and important part of every domain system. Developing quality software is critical to maintaining a stable and secure system. Most of the existing software defect prediction tasks focus on the various kinds of defects leftover in the software system, but they do not focus on the most common and frequent software defects that developers most commonly do and which have a significant effect on the quality of software development. These unnoticeable defects have a considerable impact on the functionality and also on development time, effort, and cost of the software. This paper propose a frequent software defects prediction (FSDP) mechanism based on defects Correlation learning method (CLM) utilizing various defects metrics. The aim of this work is to assist developers in accurately identifying software defects and support project managers in ensuring software quality by minimizing the presence of defect-prone code during development. The evaluation of FSDP was performed using NASA datasets in comparison with the conventional Naive Bayes, Support Vector Machine, and Random Forest classifiers and also compare with the state-of-the-arts methods to measure accuracy, precision, recall and F-Score to understand the impact of the prediction accuracy. The proposed FSDP achieves 98.88% accuracy and 98.86% precision in compare to state-of-the-arts classifiers methods indicates the effectiveness of the proposed approach in defect prediction.

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Section: Software Defect Predictionsmentioning

confidence: 99%

Section: Software Defect Predictionsmentioning

confidence: 99%

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FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

2024

IJIES

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“…Lastly, the widely-used KNNImputer model was applied to the numerical feature 'QTCOVE,' which had about 1% missing values. This imputation technique predicts missing values by observing trends in related features, making it a reliable choice (Muresan et al, 2015;Marco et al, 2021).…”

Section: Handle the Missing Datamentioning

confidence: 99%

“…Furthermore, Marco et al (2021) focus on improving data preprocessing for software effort estimation, addressing challenges related to missing data and irrelevant features in categorical variables. The research highlights the efficacy of a novel approach for improved accuracy in this domain.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container Terminal

Al Uahabi,

Attariuas,

Saleh

et al. 2024

Journal of Computer Science

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Container terminals are critical nodes within the maritime transportation system that have a vital function in global merchandise trade, handling a significant volume of cargo through the use of various equipment and personnel. Thus, the efficiency of container terminal operations relies heavily on the ability to collect, analyze, and utilize operational data. However, such data can be corrupted by noise, missing points, outliers, and incomplete or inconsistent information, making subsequent analysis or modeling challenging. This study proposes an adaptive data preprocessing framework tailored to the context of container terminal operations, using data from tangier container terminal as a case study, the leading container port in the Mediterranean and Africa, and also ranked 4 th in the CPPI 2022. This framework includes techniques for data integration, cleaning, transformation, and encoding to acquire high-quality data. In addition, the RFE feature selection method is employed to identify the most discriminative feature subset. Finally, the proposed approach, assessed using an extra tree regressor model, demonstrates strong prediction capabilities with an R-squared score of 95.4% based on the selected features for predicting the duration of vessels at port, highlighting that its integration into the terminal operating system can improve management efficiency.

show abstract

Empirical Analysis of Software Effort Preprocessing Techniques Based on Machine Learning

Cited by 2 publications

References 43 publications

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

An Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container Terminal

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