A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

Jain, Deepak Kumar; Kumar, Akshi; Sangwan, Saurabh Raj; Nguyen, Gia Nhu; Tiwari, Prayag

doi:10.1109/access.2019.2894871

Cited by 24 publications

(4 citation statements)

References 23 publications

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“…While there is a lack of application for EcBT and WMO in the last 5 years, they have been actively reviewed by work that either adopt them or strive to develop a new fault taxonomy for a different software disciplines. This is observed in EcBT [17]- [19], WMO [20], CFWA [21]- [23], OOWFT [24]- [25], WFT [25]- [26] and WAFC [27] respectively.…”

Section: Rq3: How Is the Fault Taxonomy Applied Reviewed Or Validated?mentioning

confidence: 81%

A Review on Fault Taxonomies for Web Testing

2020

IJETER

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For functional testing of web applications, fault-seeding is a method in which a software tester creates several modified versions of a web application that contain errors. They are useful for discovering faults that can compromise a web application's behaviour. Using a fault taxonomy to guide the fault-seeding of a system increases the confidence that the seeded faults are realistic. This paper presents a systematic review of fault taxonomies that are introduced for testing web applications. Selection of relevant literature is performed by applying the inclusion and exclusion criteria. Six significant fault taxonomies for web testing are then selected and analysed. We suggest three fault classes or sub-classes that can be incorporated into a fault taxonomy namely the warning notification fault, media fault and orphan page fault. Finally, some major areas where the fault taxonomies are commonly applied are health software applications, cloud computing, Android applications and desktop applications.

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Section: Rq3: How Is the Fault Taxonomy Applied Reviewed Or Validated?mentioning

confidence: 81%

A Review on Fault Taxonomies for Web Testing

2020

IJETER

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“…Unified Modeling Language (UML) can be described as a description of a system that has standard diagrams from various points of view. These diagrams include use case diagrams, use case scenarios, sequence diagrams, and class diagrams (Jain, et al, 2019).…”

Section: Unified Modeling Language (Uml)mentioning

confidence: 99%

Web-Based Academic Information Systems in Vocational School

Lengkong,

Jacobus,

Dondokambey

et al. 2023

IJITE

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An academic information system is an application that can facilitate to processing of data and information related to academic problems. Currently, SMK Negeri 1 Tondano as one of the vocational schools in North Sulawesi still uses manual methods in processing the data and information needs, so it feels less effective and efficient. This causes the data process to not be integrated properly, and it takes a long time to process data or information. This research was conducted to develop a web-based academic information system at SMK Negeri 1 Tondano. Where this information system is expected to be able to help the data and information processing process. In developing the system to be built, the research method used is the prototype development method and Unified Modeling Language (UML) modeling to simplify the design of the system to be built. Based on the results of system testing using black-box testing, it is stated that the system can run as well as expected.

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“…In FS techniques, MH is used to improve the result of classification performance ( Kashef & Nezamabadi-pour, 2015 ). Many researchers ( Larabi Marie-Sainte & Alalyani, 2020 ; Tubishat et al, 2019 ; Jain et al, 2019 ; Ahmad, Bakar & Yaakub, 2019 ; Al-Rawashdeh, Mamat & Hafhizah Binti Abd Rahim, 2019 ; Chantar et al, 2020 ; Kumar & Jaiswal, 2019 ; Singh & Kaur, 2020 ; Hassonah et al, 2020 ; Gokalp, Tasci & Ugur, 2020 ) attempt to utilize the advantages of the natural inspired MH search to discover the optimal subset feature to enhance classifier performance and decrease computational time and cost. These techniques make an effort to provide better solutions by using information from earlier iterations ( Kashef & Nezamabadi-pour, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review on meta-heuristic based feature selection techniques for text classification

Al-shalif,

Senan,

Saeed

et al. 2024

PeerJ Computer Science

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Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

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A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

Cited by 24 publications

References 23 publications

A Review on Fault Taxonomies for Web Testing

A Review on Fault Taxonomies for Web Testing

Web-Based Academic Information Systems in Vocational School

A systematic literature review on meta-heuristic based feature selection techniques for text classification

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