The ISBSG Software Project Repository: An Analysis from Six Sigma Measurement Perspective for Software Defect Estimation

Almakadmeh, Mhammed; Abran, Alain

doi:10.4236/jsea.2017.108038

Cited by 6 publications

(4 citation statements)

References 15 publications

(37 reference statements)

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“…Measure Phase is to understand current baseline performance of key variables. [26] explained measure phase is the phase for data collection development to determine issues and shortfalls. Analyze phase is the stage to evaluate the process variations, to perform root cause analysis and prioritize opportunity of improvements.…”

Section: Lean Six Sigma: Principle and Methodsmentioning

confidence: 99%

Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning

Salleh

Nohuddin

2019

IRASE

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Providing quality requirements in Software Engineering is vital to ensure the product developed is able to deploy and function to meet the operational objectives. Software Requirement Engineering is the most complex process because it involves the integration of human, logics and process. Extracting or capturing what customers need and want is called Requirement Elicitation (RE) and it is the most crucial process in requirement engineering. If handled poorly, the cost of the failures would be very expensive. Most of the software projects that failed were due to poor requirements which occurred at RE phase. Thus, enhancing and optimizing the RE methods have been subject to a long research debate to ensure quality requirements are captured. Recently, Lean Six Sigma (LSS) had emerged as part of a continuous improvement in Software Development Life Cycles (SDLC). LSS is known for a systematic and structure business improvement successfully deployed in many fields of industry that contributes a significant gain not only in quality of products and services but also in operational costs and delivery. The objective of the research is to develop an integrated conceptual framework of LSS principles with Software Requirement Engineering methodology to optimize RE process. The article will produce conceptual framework as the comprehensive guidelines to capture quality software requirements.

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Section: Lean Six Sigma: Principle and Methodsmentioning

confidence: 99%

Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning

Salleh

Nohuddin

2019

IRASE

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“…In 2017, Almakadmeh et al [23] analyzed the ISBSG MS-Excel based dataset on Six Sigma measurements and found that the ISBSG dataset has a high ratio of missing data within the data fields of the "Total Number of Defects" variable. They identified that this missing ratio represents a serious challenge when the ISBSG dataset is being used for software defect estimation.…”

Section: Background and Related Workmentioning

confidence: 99%

Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning

Haldar

Capretz

2023

2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS)

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Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. Software Defect Prediction models predict the number of defects in given projects after training the model with historical defect related information. The majority of defect prediction studies focused on predicting defect-prone modules from methods, and class-level static information, whereas this study predicts defects from project-level information based on a cross-company project dataset. This study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various machine learning algorithms. One notable issue in existing defect prediction studies is the lack of transparency in the developed models. Consequently, the explain-ability of the developed model has been demonstrated using the state-of-the-art post-hoc model-agnostic method called Shapley Additive exPlanations (SHAP). Finally, important features for predicting defects from cross-company project information were identified.

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“…Evaluation models based on characteristic parameter tracking methods have been introduced to predict equipment operation states. This involves fitting observed data linearly to obtain characteristic parameter values, thus establishing a quantitative relationship between equipment states and detection periods [9][10].…”

Section: Advancements In Lightning Protection and Operation State Eva...mentioning

confidence: 99%

“…At present, it is a very common phenomenon to use robots as auxiliary production tools in all kinds of substations, which can alleviate the heavy problem of substation inspection work to a certain extent. Safety is the core requirement of substation, which is dangerous under high voltage [9]. In order to ensure the safety of pedestrians and equipment, it is necessary to effectively detect pedestrians according to the monitoring video near the substation.…”

Section: Substation 311 Development Of Substation Inspectionmentioning

confidence: 99%

Soft Sensing Image Analysis and Processing Method of Substation Equipment Defects

Banihashemi

2023

Int. J. Appl. Inf. Manag.

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In the context of incentivizing regulation for distribution companies, the utilization of a reference network model proves to be a valuable tool for evaluating their effective cost. These models play a crucial role in planning expansive distribution areas that encompass various voltage levels. This paper introduces a green space planning algorithm designed to optimize the location, size, and power supply areas of medium and low voltage substations within the Reference Network Model (RNM). The algorithm aims to enhance the efficiency and environmental impact of these substations. The focus of this study extends to two key aspects: the creation of "environment-friendly" substations and the significance of implementing "resource-saving" substations in China. The evaluation of "environment-friendly" and "resource-saving" characteristics is conducted through comprehensive analysis, with results indicating notable features. Feature 1, associated with environmental friendliness, is measured at 0.363, while Feature 2, emphasizing resource-saving attributes, achieves a high score of 0.835. Furthermore, Feature 3, addressing the importance of implementing these eco-friendly substations in the Chinese context, attains a commendable score of 0.824. The findings underscore the potential of the proposed green space planning algorithm in enhancing the sustainability and efficiency of medium and low voltage substations within the incentive regulation framework for distribution companies.

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The ISBSG Software Project Repository: An Analysis from Six Sigma Measurement Perspective for Software Defect Estimation

Cited by 6 publications

References 15 publications

Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning

Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning

Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning

Soft Sensing Image Analysis and Processing Method of Substation Equipment Defects

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