A Case Study on Implementing Modularity in Software Development

Sun, Hongyi; Ha, Waileung; Teh, Pei Lee; Huang, Jianglin

doi:10.1080/08874417.2016.1183430

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…Specifically for prevention and control of crop diseases, pests and weeds, many papers were published demonstrating the application of computer vision and deep learning on agriculture (e.g. Maharlooei et al [4], Liu and Chahl [5], Zhong et al [6]).…”

Section: Computer Vision and Deep Learning For Prevention Of Olive Pests And Diseasesmentioning

confidence: 99%

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Alves

Morais

Filipe

et al. 2021

Lecture Notes in Networks and Systems

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Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os-Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases.Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

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Section: Computer Vision and Deep Learning For Prevention Of Olive Pests And Diseasesmentioning

confidence: 99%

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Alves

Morais

Filipe

et al. 2021

Lecture Notes in Networks and Systems

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Development of computational models of emotions: A software engineering perspective

Osuna

Rodríguez

Gutiérrez-García

2020

Cognitive Systems Research

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Optimizing defect rework for a software start-up's schedule variation: a Six-Sigma-based approach

Mahato,

Roy

2024

IJQRM

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PurposeManaging project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.Design/methodology/approachAn optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).FindingsThe output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.Research limitations/implicationsThis work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.Practical implicationsThe Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.Originality/valueThis paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

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A Case Study on Implementing Modularity in Software Development

Cited by 6 publications

References 38 publications

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Development of computational models of emotions: A software engineering perspective

Optimizing defect rework for a software start-up's schedule variation: a Six-Sigma-based approach

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