Software Defect Prediction: A Comparative Analysis of Machine Learning Techniques

Shrimankar, Radhika; Kuanr, Madhusree; Piri, Jayashree; Panda, Niranjan

doi:10.1109/mlcss57186.2022.00016

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Their findings, which achieved high accuracy with simple financial ratios, indicate the effectiveness of ML in analyzing financial data, a key component in tax prediction. Shrimankar et al (2022) conducted a comparative analysis of various ML techniques for software defect prediction. They found that gradient boosting and logistic regression provided superior performance among other classifiers.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Application of machine learning in tax prediction: A review with practical approaches

Olatunji Akinrinola,

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise

et al. 2024

Global J. Eng. Technol. Adv.

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This scholarly exploration delves into the dynamic intersection of machine learning (ML) and tax prediction, a realm where the fusion of advanced computational techniques and fiscal analytics heralds a new epoch in financial forecasting. The study's purpose was to meticulously dissect the efficacy of ML in tax prediction, navigating through the complexities and potentialities embedded within this innovative domain. The scope of the paper encompasses a comprehensive review of the evolution of tax prediction models, the significance of accurate tax forecasting in economic planning, and a critical analysis of previous studies, thereby laying a robust foundation for understanding the current state and future trajectory of ML in taxation. Employing a methodical approach, the study conducted a systematic review of peer-reviewed literature, focusing on the comparative analysis of various ML models, the impact of data quality on predictions, and innovative approaches in ML for enhanced tax prediction. This rigorous methodology ensured a holistic understanding of the subject matter, providing insights into both the theoretical underpinnings and practical applications of ML in tax forecasting. The main findings reveal that while ML presents unparalleled opportunities in tax prediction, it is not devoid of challenges such as data complexity, model interpretability, and ethical considerations. The study concludes that the integration of ML in tax prediction can significantly enhance accuracy and efficiency, provided these challenges are meticulously addressed. Recommendations include the adoption of appropriate ML models, emphasis on high-quality data, continuous model evaluation, and adherence to ethical practices in ML modeling. In summary, this paper offers a comprehensive and nuanced perspective on the role of ML in revolutionizing tax prediction. It serves as a guiding framework for policymakers, tax authorities, and researchers, advocating for a harmonious blend of technological innovation and ethical responsibility in the pursuit of advanced tax forecasting methods.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Application of machine learning in tax prediction: A review with practical approaches

Olatunji Akinrinola,

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise

et al. 2024

Global J. Eng. Technol. Adv.

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“…Related to the fourth phase of SDLC, implementation, ML techniques can be u can be applied for a variety of purposes. these include assisting in the identification of weak points in source code (Sonnekalb 2019) and helping to predict defects in software (Ahmed et al 2020;Pradhan and Nanniyur 2021;Shrimankar et al 2022), generating code using generative AI (Sun et al 2022), and other techniques such as NLP which is used to classifying bugs (Picus and Serban 2022) and the use of tools such as ChatGPT which helps in code analysis (Ozturk et al 2023). Regarding the testing phase, several forms of this application are the use of DL techniques which are used to automate the process of generating test case scenarios (Roy et al 2021), the test case classification process by utilizing a combination of NLP and ML techniques (Tahvili et al 2020), as well as the use of NLP to provide solutions and automate fixing in the source code (Chi et al 2023).…”

Section: The Current State Of Ai Technique Application In Sdlcmentioning

confidence: 99%

Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle

Giffari,

Ridho,

Indra Sensuse

et al. 2024

J. Sistem Inf. (J. Inf. Sys.)

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Artificial Intelligence (AI) integration has been the goal in many industries, including in the software development industry. One example of this integration comes in the form of integrating AI in the Software Development Lifecycle (SDLC). To date, the difficulties of incorporating AI-based tools into particular phases of SDLC have not received much attention in research. Using qualitative approach, this study aims to discover the perception on the use of AI-based tools and challenges in integrating them in the analysis phase of SDLC. The study finds out that analyst have positive perception about integrating this technology in their field of work but there are some challenges while integrating this technology such as data security and reliability concern, dependency, and adapting to this technology. This study also discovers some key factors of why some analysts adopt or refuse this technology namely time, urgency, budget, and insecurities of the users.

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Software Defect Prediction using ANN Algorithm

Shankar,

Sivakumar,

Dhabliya

et al. 2023

2023 7th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Software Defect Prediction: A Comparative Analysis of Machine Learning Techniques

Cited by 3 publications

References 18 publications

Application of machine learning in tax prediction: A review with practical approaches

Application of machine learning in tax prediction: A review with practical approaches

Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle

Software Defect Prediction using ANN Algorithm

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