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16th IEEE international conference on software Testing, Verification and Validation (ICST)

作者： Dobslaw, Felix Feldt, Robert Mid Sweden Univ Dept Commun Qual Mngmt & Inf Syst Ostersund Sweden Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9798350333350

This study examines to what extent the testing of traditional software components and machine learning (ML) models fundamentally differs or not. While some researchers argue that ML software requires new concepts and perspectives for testing, our analysis highlights that, at a fundamental level, the specification and testing of a software component are not dependent on the development process used or on implementation details. Although the software engineering/computer science (SE/CS) and Data Science/ML (DS/ML) communities have developed different expectations, unique perspectives, and varying testing methods, they share clear commonalities that can be leveraged. We argue that both areas can learn from each other, and a non-dual perspective could provide novel insights not only for testing ML but also for testing traditional software. Therefore, we call upon researchers from both communities to collaborate more closely and develop testing methods and tools that can address both traditional and ML software components. While acknowledging their differences has merits, we believe there is great potential in working on unified methods and tools that can address both types of software.

关键词： software Testing software engineering machine learning Non-Duality software Boundaries

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Using Bigrams to Detect Leaked Secrets in Source Code 18

Using Bigrams to Detect Leaked Secrets in Source Code

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Konygin, Anton V. Kopnin, Andrey V. Mezentsev, Ilya P. Pankratov, Alexandr A. SKB Kontur Ekaterinburg Russia NN Krasovskii Inst Math & Mech Ekaterinburg Russia

ISBN: (纸本)9789897586477

Leaked secrets in source code lead to information security problems. It is important to find sensitive information in the repository as early as possible and neutralize it. By now, there are many different approaches to leaked secret detection without human intervention. Often, these are heuristic algorithms using regular expressions. Recently, more and more approaches based on machine learning have appeared. Nevertheless, the problem of detecting secrets in the code remains relevant since the available approaches often give a large number of false positives. In this paper, we propose an approach to leaked secret detection in source code based on machine learning using bigrams. This approach significantly reduces the number of false positives. The model showed a false positive rate of 2.4% and false negative rate of 1.9% on test dataset.

关键词： Leaked Secrets Source Code machine learning Security

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Metric learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error 16

Metric Learning Improves the Ability of Combinatorial Covera...

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16th IEEE international conference on software Testing, Verification and Validation (ICST)

作者： Cody, Tyler Freeman, Laura Virginia Tech Natl Secur Inst Arlington VA 22203 USA

ISBN: (纸本)9798350333350

machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Out-of-distribution (OOD) data is known to increase the probability of error and research into metrics that identify what dissimilarities in data affect model performance is on-going. Recently, combinatorial coverage metrics have been explored in the literature as an alternative to distribution-based metrics. Results show that coverage metrics can correlate with classification error. However, other results show that the utility of coverage metrics is highly dataset-dependent. In this paper, we show that this datasetdependence can be alleviated with metric learning, a machine learning technique for learning latent spaces where data from different classes is further apart. In a study of 6 open-source datasets, we find that metric learning increased the difference between set-difference coverage metrics (SDCCMs) calculated on correctly and incorrectly classified data, thereby demonstrating that metric learning improves the ability of SDCCMs to anticipate classification error. Paired t-tests validate the statistical significance of our findings. Overall, we conclude that metric learning improves the ability of coverage metrics to anticipate classifier error and identify when OOD data is likely to degrade model performance.

关键词： Terms combinatorial coverage combinatorial testing machine learning out-of-distribution (OOD)

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ICSOB-C 2023 - Companion Proceedings of the 14th international conference on software Business (PhD Retreat, Posters and Demos Track)

ICSOB-C 2023 - Companion Proceedings of the 14th Internation...

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14th international conference on software Business, ICSOB-C 2023

The proceedings contain 17 papers. The topics discussed include: creating happier and more productive software engineering teams through AI and machine learning;hybrid work in agile software development;ai assistant to improve experimentation in software startups using large language model and prompt engineering;improving software startup viability: addressing requirements prioritization challenges amid increasing interest rates;the impact of DevOps critical success factors and organizational practices;diversity, equity and inclusion in the age of generative AI;exploring the 6G software business ecosystem: a morphological analysis approach;digital sovereignty from the perspective of IT consultancy in Germany: a model;the effects of quality of technology on learning performance in remote lectures;and transforming software products for intelligent automation services.

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Defectors: A Large, Diverse Python Dataset for Defect Prediction 20

Defectors: A Large, Diverse Python Dataset for Defect Predic...

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IEEE/ACM 20th international conference on Mining software Repositories (MSR)

作者： Mahbub, Parvez Shuvo, Ohiduzzaman Rahman, Mohammad Masudur Dalhousie Univ Halifax NS Canada

ISBN: (纸本)9798350311846

Defect prediction has been a popular research topic where machine learning (ML) and deep learning (DL) have found numerous applications. However, these ML/DL-based defect prediction models are often limited by the quality and size of their datasets. In this paper, we present Defectors, a large dataset for just-in-time and line-level defect prediction. Defectors consists of approximate to 213K source code files (approximate to 93K defective and approximate to 120K defect-free) that span across 24 popular Python projects. These projects come from 18 different domains, including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for training ML/DL models, especially transformer models that require large and diverse datasets. We also foresee several application areas of our dataset including defect prediction and defect explanation.

关键词： Defect Prediction Just-in-Time Dataset software engineering

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A Systematic Approach for Enhancing software Defect Prediction Using machine learning

A Systematic Approach for Enhancing Software Defect Predicti...

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2023 international conference on Next-Generation Computing, IoT and machine learning, NCIM 2023

作者： Rifat, S.M. Bhuyain, Akil Uddin Hossain, Md. Shamim Mia, Md. Solaiman Rahman, Mahmuda Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

In the modern world of software development, ensuring reliability and performance is of paramount importance. However, despite the best efforts from the developers, software defects can still emerge, causing frustration and wasted resources. Due to the numerous defects found during the software development process, researchers have developed numerous ways for defect prediction models. However, these models cut down the time and expense of development when problems in a concurrent software product are anticipated. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. The study of this paper has shown that a combination of machine learning algorithms could be applied effectively for software defect prediction. Interestingly, the combination of Artificial Neural Network and Random Forest classifier has been performed with the mean accuracy of 91%, while the hyper-parameter optimization model classifier has been performed with the mean accuracy of 83%, 83%, 84%, 77% and 80% for Support Vector machine, Random Forest, Logistic Regression, Naive Bayes Gaussian and Decision Tree, respectively. These findings have demonstrated the potential of machine learning in the area of software development. © 2023 IEEE.

关键词： Defects

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A Detailed Review and Study of Implementation of Security System Design for machine learning-Based software Systems: Issues And Challenges 3

A Detailed Review and Study of Implementation of Security Sy...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Lourens, Melanie Tripathi, Vikas Alanya-Beltran, Joel Akram, Shaik Vaseem Bhaskar, Bandi Firos, A. Durban University of Technology Department of Human Resources Management Berea Durban South Africa Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India Universidad Tecnológica del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India SR University School of CS& AI Telangana Warangal India Department of Computer Science and Engineering Arunachal Pradesh Doimukh791112 India

ISBN: (纸本)9798350399264

The rapidly growing technology specially in the field of software, machine learning (ML) has played an important role in a range of tasks, including voice, video, and computer vision. It is currently being utilised in software systems to automate the crucial processes more and more. machine learning-based modern software systems (MLBSS) are currently difficult to build safely, which will severely limit the uses in security and safety-critical domains. Recently, majority of articles are published and still research work is going on the safety problems for ML and Deep learning (DL), which place a strong prominence on the models and data both, adversaries' threats have been taken into consideration. In this paper, we address the prospect that system bugs or external adversarial assaults might lead to security vulnerabilities for machine learning-based software systems, and we propose that safe development techniques ought to be applied throughout the whole lifecycle. We conclude by providing a thorough study of the security for MLBSS, which includes a comprehensive analysis based on a review of the structure of three distinctive features in terms of security issues. The entire state-of-the-art for MLBSS secure development is also provided. © 2023 IEEE.

关键词： Computer software

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A Systematic Survey on Breast Cancer Detection Using machine learning 6

A Systematic Survey on Breast Cancer Detection Using Machine...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Chauhan, Vishal Shukla, Rohit Ansari, Shahabudin Bindiya, Anchal Gupta, Manish Kumar Buddha Institute of Technology Department of Information Technology Gorakhpur India Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350304480

Approximately 270,000 new cases of Breast cancer, the most common cancer in women, are reported each year, with instances identified in 2022. software for detection is required to find it before it becomes dead because of this. The software can be created to identify this dangerous cancer and treat it before it results in the patient's death by using machine learning algorithms. Additionally, Breast canceris the most common cancer among Indian women. A lady with breast cancerhas a 50% chance of survival. Many machine-learning techniques can be used to detect BC. This survey paper provides a summary of different ML techniques used for detecting breast cancer. And also provide a comparative analysis of different machine-learning techniques in the field of breast cancer detection. © 2023 IEEE.

关键词： Algorithms BC Cancer machine learning software

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machine learning-BASED MODELING OF ELECTRIC-FIELD-ASSISTED DIRECT INK WRITING (EDIW) PROCESS 18

MACHINE LEARNING-BASED MODELING OF ELECTRIC-FIELD-ASSISTED D...

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ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Chen, Yinong Deshpande, Anupam Ajit Joyee, Erina Baynojir Pan, Yayue Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Univ N Carolina Dept Mech Engn & Engn Sci Charlotte NC USA

ISBN: (纸本)9780791887233

Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented.

关键词： Electric-field-assisted direct ink writing (eDIW) machine learning Process modelling Artificial neural network (ANN)

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AI Living Lab: Quality Assurance for AI-based Health systems 2

AI Living Lab: Quality Assurance for AI-based Health systems

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Lenarduzzi, Valentina Isomursu, Minna Univ Oulu Oulu Finland

ISBN: (纸本)9798350301137

The main goal of this project is to develop an AI Living Lab providing methods and software tools for AI trustworthiness analysis, running digital twins to simulate Digital Health solutions (Hardware and software) integrated with AI elements in vitro for early-stage validation experiments. In this paper, we present the motivation beyond the need of a AI Living Lab methods for researchers and companies, our idea in practice, and the scheduled roadmap. The insights of the AI Living Lab can enable researchers to understand possible problems on the quality of AI-enabled systems opening new research topics and allows companies to understand how to better address quality issues in their systems.

关键词： Artificial Intelligence machine learning software Quality software engineering Health

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