Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design

Chua, Fang-Fang; Lim, Tek-Yong; Tajuddin, Bushra Binti; Yanuarifiani, Amarilis Putri

doi:10.33093/jiwe.2022.1.1.1

Cited by 5 publications

(3 citation statements)

References 12 publications

(22 reference statements)

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“…Furthermore, the system can detect vehicles that commit traffic violations, such as illegal U-turns, overspeeding, and running red lights, among others. We could enhance our software design process by integrating the semi-automated approach proposed in [19]. Additionally, we can optimize our development methodology by adopting the most effective agile practices outlined in [20].…”

Section: Discussionmentioning

confidence: 99%

Traffic Impact Assessment System using Yolov5 and ByteTrack

Ng,

Goh,

Tee

2023

JIWE

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Monitoring software for traffic is not too much in this era of digital. Even cheaper is decent traffic monitoring software. You can gauge the quality of the software. It should be possible to assess the code's performance outside of a test environment. The most useful metrics are frequently those that support the program's ability to fulfil business requirements. Therefore, this project is planning to develop a traffic assessment system. The main purpose of development is to improve heavy traffic in this country – Malaysia. This system includes function vehicle detection using YOLOv5, vehicle counting with a different type (such as bus, car, truck), vehicle classification, vehicle idling time by each region, and vehicle counting for each junction. Users can draw regions and lines for each camera/video to count and record vehicles. After the traffic analysis, intelligent signal light systems that respond to loads and timing can be helpful in easing traffic congestion. Smart traffic lights may adapt to the patterns of bustle at junctions and other important road traffic places based on the number of cars, data from queue detectors, and images from cameras. Also, this report includes comparisons with StrongSORT, OC-SORT and ByteTrack and accuracy test for vehicle counting.

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Section: Discussionmentioning

confidence: 99%

Traffic Impact Assessment System using Yolov5 and ByteTrack

Ng,

Goh,

Tee

2023

JIWE

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“…It is based on statistical learning theory and structural risk minimization [16], which makes it more suitable for limited training data compared to other machine learning classifiers [17]. Dropout prediction is essentially a problem of identifying two types of data, and SVM is primarily designed to find the optimal hyperplane for two types of data and maximize the separation between them [18]. The algorithm of SVM is shown in Figure 1.…”

Section: A Support Vector Machinementioning

confidence: 99%

Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM

Yujiao,

Ang,

Shaomin

et al. 2023

JIWE

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Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal weights to each behavioral characteristic, despite the fact that each behavioral characteristic has a different effect on predicting dropout. To address this problem, this paper proposes a dropout prediction model based on the fusion of behavioral data and Support Vector Machine (SVM). This innovative model assigns different weights to different behavior features based on Pearson principle and integrates them as data inputs to the model. Dropout prediction is essentially a binary problem, Support Vector Machine Classifier is then trained using the training dataset 1 and dataset 2. Experimental results on both datasets show that this predictive model outperforms previous models that assign the same weights to the behavior features.

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“…Similarly, the Value-Oriented method [30] focuses on elements of core value for the business. Additionally, two more recent methods include DLM-MLSRP [31] that considers aspects of cost and benefit, within a set of pre-fixed aspects, and uses the deep neural Lagrange multiplier to establish the ranking of requirements; and the SARiP method [32] that is based on an initial prioritization using the MosCoW method and the assessment of a set of predefined aspects. The latter method uses machine learning for the final estimation of prioritization around three groups (low, medium and high priority) and the corresponding ranking in each group.…”

Section: Related Workmentioning

confidence: 99%

OurRank: A Software Requirements Prioritization Method Based on Qualitative Assessment and Cost-Benefit Prediction

Rojas¹,

Olivares-Rodríguez

Álvarez

et al. 2022

IEEE Access

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Requirements prioritization is an activity aimed at determining the essential requirements to include in a software release. Although there are several prioritization methods to systematize this task, there are still unresolved challenges. Existing methods do not guarantee that requirements prioritization meets stakeholder expectations and goals. This is because most prioritization methods operate by considering only quantitative information, making it difficult to formally capture stakeholder interests and perspectives that can rather be made explicit in qualitative terms. Likewise, methods including qualitative information only consider elements associated with benefit estimation, that is, positive aspects of the project, but neglect costs or negative aspects. As a result, the prioritization process is driven by a partial view of constraints. Such methods also fail at capturing and combining expert knowledge that decision-makers can bring into the decision-making process. In this research, we propose a novel method for software requirements prioritization, which facilitates incorporating experts' qualitative assessment at the outset of the prioritization process and considers both benefit and cost constraints. Details of the method are presented, together with a case study describing a real application scenario. Recommendations and guidelines regarding the application of the method are proposed based on the results of the case study.INDEX TERMS Cost-benefit prediction, qualitative prioritization criteria, prioritization method, requirements prioritization.

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Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design

Cited by 5 publications

References 12 publications

Traffic Impact Assessment System using Yolov5 and ByteTrack

Traffic Impact Assessment System using Yolov5 and ByteTrack

Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM

OurRank: A Software Requirements Prioritization Method Based on Qualitative Assessment and Cost-Benefit Prediction

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