Our research is set in the industrial context of Nokia 5G and the introduction of Machine Learning software Defect Prediction (ML SDP) to the existing quality assurance process within the company. We aim to support or...
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ISBN:
(纸本)9798400706585
Our research is set in the industrial context of Nokia 5G and the introduction of Machine Learning software Defect Prediction (ML SDP) to the existing quality assurance process within the company. We aim to support or undermine the profitability of the proposed ML SDP solution designed to complement the system-level black-box testing at Nokia, as cost-effectiveness is the main success criterion for further feasibility studies leading to a potential commercial introduction. To evaluate the expected cost-effectiveness, we utilize one of the available cost models for software defect prediction formulated by previous studies on the subject. Second, we calculate the standard Return on Investment (ROI) and Benefit-Cost Ratio (BCR) financial ratios to demonstrate the proftability of the developed approach based on real-world, business-driven examples. Third, we build an MS Excel-based tool to automate the evaluation of similar scenarios that other researchers and practitioners can use. We considered different periods of operation and varying effciency of predictions, depending on which of the two proposed scenarios were selected (lightweight or advanced). Performed ROI and BCR calculations have shown that the implemented ML SDP can have a positive monetary impact and be cost-effective in both scenarios. The cost of adopting new technology is rarely analyzed and discussed in the existing scientific literature, while it is vital for many software companies worldwide. Accordingly, we bridge emerging technology (machine learning software defect prediction) with a softwareengineering domain (5G system-level testing) and business considerations (cost efficiency) in an industrial environment of one of the leaders in 5G wireless technology.
[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the develop...
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ISBN:
(数字)9783031297861
ISBN:
(纸本)9783031297854;9783031297861
[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. [Question/problem] We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. [Principal ideas/results] Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. [Contribution] The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.
To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed ...
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software practitioners must implement a growing list of regulatory and security mandates, but have no established tool or mechanism for demonstrating their due diligence or compliance efforts exists. Providing an appr...
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ISBN:
(数字)9781665470001
ISBN:
(纸本)9781665470001
software practitioners must implement a growing list of regulatory and security mandates, but have no established tool or mechanism for demonstrating their due diligence or compliance efforts exists. Providing an approach does more than help software practitioners. External agencies and auditors also need tools or mechanisms to enforce compliance requirements. Consumers also benefit. Standardized approaches a mechanism for accountability regarding compliance without software organizations compromising its proprietary or sensitive information. Currently, perceptions, practices, or decision making on regulatory or security standard compliance is not a well researched area in academia. Our research aims to understand the practices and decision making software organizations apply toward regulatory compliance requirements during the software development process. Then, we take this improved understanding and apply it to building an approach that auditors or regulators can use to validate regulatory compliance throughout the entire software development process.
Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order...
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Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely ...
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Open-source software (OSS) has been extensively employed to expedite software development, inevitably exposing downstream software to the peril of potential vulnerabilities. Precisely identifying the version of OSS no...
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Automobile insurance fraud causes huge economic losses to insurance companies, detection technology has become an urgent research topic. However, feature selection manually is prone to subjective judgment, which resul...
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Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically...
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