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Auflistung nach Schlagwort "Costs"

1 - 3 von 3
  • ConferencePaper
    On the Cost and Profit of Software Defect Prediction
    (Software Engineering 2021, 2021) Herbold, Steffen
    The article "On the cost and profit of software defect prediction" published in the IEEE Transactions on Software Engineering in 2019 propose a cost model to enable the estimation of the expected profit when using machine learning models for defect prediction. Defect prediction can be a powerful tool to guide the use of quality assurance resources. However, while lots of research covered methods for defect prediction as well as methodological aspects of defect prediction research, the actual cost saving potential of defect prediction is still unclear. We close this research gap and formulate a cost model for software defect prediction. We derive mathematically provable boundary conditions that must be fulfilled by defect prediction models such that there is a positive profit when the defect prediction model is used. Our cost model includes aspects like the costs for quality assurance, the costs of post-release defects, the possibility that quality assurance fails to reveal predicted defects. Our results show that the unrealistic assumption that defects only affect a single software artifact leads to inaccurate cost estimations. Moreover, the results indicate that thresholds for machine learning metrics are also not suited to define success criteria for software defect prediction.
  • Konferenzbeitrag
    On the validity of pre-trained transformers for natural language processing in the software engineering domain
    (Software Engineering 2023, 2023) von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen
    We summarize the article On the validity of pre-trained transformers for natural language processing in the software engineering domain [VTH22], which was published in the IEEE Transactions on Software Engineering in 2022.
  • Zeitschriftenartikel
    The Cost of Fairness in AI: Evidence from E-Commerce
    (Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Zahn, Moritz; Feuerriegel, Stefan; Kuehl, Niklas
    Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness�? have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.