Survey on Risk Classification in Agile Software Development Projects in Latvia

Nikiforova, Oksana; Babris, Kristaps; Kristapsons, Jānis

doi:10.2478/acss-2020-0012

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Daily Standup Meetings [10,28] Sprint Planning [10,3,23,11] Brainstorming [23,22,9] Checklist [22] Weekly Sprint Meetings [16] Increment [17] Prototype [17] Product backlog refinement meeting [17,28,4] Weekly risk meeting [17] Technical specification [17] Risk Identification Meetings [23] Sprint backlog [28] Sprint review [14,3,4,18] Sprint Retrospective [3]…”

Section: Practices Referencesmentioning

confidence: 99%

“…Creative teams with diversity in expertise are the key to success in brainstorming [17]. However, they must give up intentional ignorance for team collaboration to be effective [18]. As a result, risk identification assistance tools must consider the above constraints and facilitate communication and collaboration among participants [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Head CNN-based Software Development Risk Classification

Ziana M.,

2023

Int. j. electr. comput. eng. syst. (Online)

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Agile methodology for software development has been in vogue for a few decades, notably among small and medium enterprises. The omission of an explicit risk identification approach turns a blind eye to a range of perilous risks, thus dumping the management into strenuous situations and precipitating dreadful issues at the crucial stages of the project. To overcome this drawback a novel Agile Software Risk Identification using Deep learning (ASRI-DL) approach has been proposed that uses a deep learning technique along with the closed fishbowl strategy, thus assisting the team in finding the risks by molding them to think from diverse perspectives, enhancing wider areas of risk coverage. The proposed technique uses a multi-head Convolutional Neural Network (Multihead-CNN) method for classifying the risk into 11 classes such as over-doing, under-doing, mistakes, concept risks, changes, differences, difficulties, dependency, conflicts, issues, and challenges in terms of producing a higher number of risks concerning score, criticality, and uniqueness of the risk ideas. The descriptive statistics further demonstrate that the participation and risk coverage of the individuals in the proposed methodology exceeded the other two as a result of applying the closed fishbowl strategy and making use of the risk identification aid. The proposed method has been compared with existing techniques such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Generalized Linear Models (GLM), and CNN using specific parameters such as accuracy, specificity, and sensitivity. Experimental findings show that the proposed ASRI-DL technique achieves a classification accuracy of 99.16% with a small error rate with 50 training epochs respectively.

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