Relevant bibliographies by topics / Computer software. Software engineering. Machine learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Computer software. Software engineering. Machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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1

Cao, Bingfei. "Augmenting the software testing workflow with machine learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119752.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 67-68).<br>This work presents the ML Software Tester, a system for augmenting software testing processes with machine learning. It allows users to plug in a Git repository of the choice, specify a few features and methods sp
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Brun, Yuriy 1981. "Software fault identification via dynamic analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 65-67).<br>I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy
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Bayana, Sreeram. "Learning to deal with COTS (commercial off the shelf)." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3859.

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Thesis (M.S.)--West Virginia University, 2005<br>Title from document title page. Document formatted into pages; contains vii, 66 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 61-66).
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Liljeson, Mattias, and Alexander Mohlin. "Software defect prediction using machine learning on test and source code metrics." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4162.

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Context. Software testing is the process of finding faults in software while executing it. The results of the testing are used to find and correct faults. Software defect prediction estimates where faults are likely to occur in source code. The results from the defect prediction can be used to opti- mize testing and ultimately improve software quality. Machine learning, that concerns computer programs learning from data, is used to build pre- diction models which then can be used to classify data. Objectives. In this study we, in collaboration with Ericsson, investigated whether software metri
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Chi, Yuan. "Machine learning techniques for high dimensional data." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2033319/.

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This thesis presents data processing techniques for three different but related application areas: embedding learning for classification, fusion of low bit depth images and 3D reconstruction from 2D images. For embedding learning for classification, a novel manifold embedding method is proposed for the automated processing of large, varied data sets. The method is based on binary classification, where the embeddings are constructed so as to determine one or more unique features for each class individually from a given dataset. The proposed method is applied to examples of multiclass classifica
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Richmond, James Howard. "Bayesian Logistic Regression Models for Software Fault Localization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1326658577.

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Kaloskampis, Ioannis. "Recognition of complex human activities in multimedia streams using machine learning and computer vision." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/59377/.

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Modelling human activities observed in multimedia streams as temporal sequences of their constituent actions has been the object of much research effort in recent years. However, most of this work concentrates on tasks where the action vocabulary is relatively small and/or each activity can be performed in a limited number of ways. In this Thesis, a novel and robust framework for modelling and analysing composite, prolonged activities arising in tasks which can be effectively executed in a variety of ways is proposed. Additionally, the proposed framework is designed to handle cognitive tasks,
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Hossain, Md Billal. "QoS-Aware Intelligent Routing For Software Defined Networking." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1595086618729923.

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9

Percival, Graham Keith. "Physical modelling meets machine learning : performing music with a virtual string ensemble." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4253/.

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This dissertation describes a new method of computer performance of bowed string instruments (violin, viola, cello) using physical simulations and intelligent feedback control. Computer synthesis of music performed by bowed string instruments is a challenging problem. Unlike instruments whose notes originate with a single discrete excitation (e.g., piano, guitar, drum), bowed string instruments are controlled with a continuous stream of excitations (i.e. the bow scraping against the string). Most existing synthesis methods utilize recorded audio samples, which perform quite well for single-exc
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Osgood, Thomas J. "Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/60439/.

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At the highest level the aim of this thesis is to review and develop reliable and efficient algorithms for classifying road scenery primarily using vision based technology mounted on vehicles. The purpose of this technology is to enhance vehicle safety systems in order to prevent accidents which cause injuries to drivers and pedestrians. This thesis uses LIDAR–stereo sensor fusion to analyse the scene in the path of the vehicle and apply semantic labels to the different content types within the images. It details every step of the process from raw sensor data to automatically labelled images.
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Grills, Blake E. "Automatic Identification and Analysis of Commented Out Code." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587646144001317.

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Shahdad, Mir Abubakr. "Engineering innovation (TRIZ based computer aided innovation)." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3317.

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This thesis describes the approach and results of the research to create a TRIZ based computer aided innovation tools (AEGIS and Design for Wow). This research has mainly been based around two tools created under this research: called AEGIS (Accelerated Evolutionary Graphics Interface System), and Design for Wow. Both of these tools are discussed in this thesis in detail, along with the test data, design methodology, test cases, and research. Design for Wow (http://www.designforwow.com) is an attempt to summarize the successful inventions/ designs from all over the world on a web portal which
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Ewö, Christian. "A machine learning approach in financial markets." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5571.

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In this work we compare the prediction performance of three optimized technical indicators with a Support Vector Machine Neural Network. For the indicator part we picked the common used indicators: Relative Strength Index, Moving Average Convergence Divergence and Stochastic Oscillator. For the Support Vector Machine we used a radial-basis kernel function and regression mode. The techniques were applied on financial time series brought from the Swedish stock market. The comparison and the promising results should be of interest for both finance people using the techniques in practice, as well
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Robeson, Aaron. "Airwaves: A Broadcasting Web Application Supplemented by a Neural Network Transcription Model." Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors155603038153628.

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Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.

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In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection tec
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Le, Khanh Duc. "A Study of Face Embedding in Face Recognition." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/1989.

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Face Recognition has been a long-standing topic in computer vision and pattern recognition field because of its wide and important applications in our daily lives such as surveillance system, access control, and so on. The current modern face recognition model, which keeps only a couple of images per person in the database, can now recognize a face with high accuracy. Moreover, the model does not need to be retrained every time a new person is added to the database. By using the face dataset from Digital Democracy, the thesis will explore the capability of this model by comparing it with the s
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Fahlén, Erik. "Androidapplikation för digitalisering av formulär : Minimering av inlärningstid, kostnad och felsannolikhet." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35623.

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This study was performed by creating an android application that uses custom object recognition to scan and digitalize a series of checkbox form for example to correct multiple-choice questions or collect forms in a spreadsheet. The purpose with this study was to see which dataset and hardware with the machine learning library TensorFlow was cheapest, price worthy, enough reliable and fastest. A dataset of filled example forms with annotated checkboxes was created and used in the learning process. The model that was used for the object recognition was Single Show MultiBox Detector, MobileNet v
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Hsu, Samantha. "CLEAVER: Classification of Everyday Activities Via Ensemble Recognizers." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1960.

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Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these me
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Gokyer, Gokhan. "Identifying Architectural Concerns From Non-functional Requirements Using Support Vector Machine." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609964/index.pdf.

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There has been no commonsense on how to identify problem domain concerns in architectural modeling of software systems. Even, there is no commonly accepted method for modeling the Non-Functional Requirements (NFRs) effectively associated with the architectural aspects in the solution domain. This thesis introduces the use of a Machine Learning (ML) method based on Support Vector Machines to relate NFRs to classified &quot<br>architectural concerns&quot<br>in an automated way. This method uses Natural Language Processing techniques to fragment the plain NFR texts under the supervision of domain
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Ekström, Hagevall Adam, and Carl Wikström. "Increasing Reproducibility Through Provenance, Transparency and Reusability in a Cloud-Native Application for Collaborative Machine Learning." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435349.

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The purpose of this thesis paper was to develop new features in the cloud-native and open-source machine learning platform STACKn, aiming to strengthen the platform's support for conducting reproducible machine learning experiments through provenance, transparency and reusability. Adhering to the definition of reproducibility as the ability of independent researchers to exactly duplicate scientific results with the same material as in the original experiment, two concepts were explored as alternatives for this specific goal: 1) Increased support for standardized textual documentation of machin
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Waqas, Muhammad. "A simulation-based approach to test the performance of large-scale real time software systems." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20133.

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Background: A real-time system operates with time constraints, and its correctness depends upon the time on which results are generated. Different industries use different types of real-time systems such as telecommunication, air traffic control systems, generation of power, and spacecraft system. There is a category of real-time systems that are required to handle millions of users and operations at the same time; those systems are called large scale real-time systems. In the telecommunication sector, many real-time systems are large scale, as they need to handle millions of users and resourc
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Salem, Tawfiq. "Learning to Map the Visual and Auditory World." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/86.

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The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabil
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França, André Luiz Pereira de. "Estudo, desenvolvimento e implementação de algoritmos de aprendizagem de máquina, em software e hardware, para detecção de intrusão de rede: uma análise de eficiência energética." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1166.

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CAPES; CNPq<br>O constante aumento na velocidade da rede, o número de ataques e a necessidade de eficiência energética estão fazendo com que a segurança de rede baseada em software chegue ao seu limite. Um tipo comum de ameaça são os ataques do tipo probing, nos quais um atacante procura vulnerabilidades a partir do envio de pacotes de sondagem a uma máquina-alvo. Este trabalho apresenta o estudo, o desenvolvimento e a implementação de um algoritmo de extração de características dos pacotes da rede em hardware e de três classificadores de aprendizagem de máquina (Árvore de Decisão, Naive Bayes
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Håkansson, Fredrik, and Carl-Johan Larsson. "User-Based Predictive Caching of Streaming Media." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151008.

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Streaming media is a growing market all over the world which sets a strict requirement on mobile connectivity. The foundation for a good user experience when supplying a streaming media service on a mobile device is to ensure that the user can access the requested content. Due to the varying availability of mobile connectivity measures has to be taken to remove as much dependency as possible on the quality of the connection. This thesis investigates the use of a Long Short-Term Memory machine learning model for predicting a future geographical location for a mobile device. The predicted locati
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Farhat, Md Tanzin. "An Artificial Neural Network based Security Approach of Signal Verification in Cognitive Radio Network." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo153511563131623.

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Bessinger, Zachary. "An Automatic Framework for Embryonic Localization Using Edges in a Scale Space." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1262.

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Localization of Drosophila embryos in images is a fundamental step in an automatic computational system for the exploration of gene-gene interaction on Drosophila. Contour extraction of embryonic images is challenging due to many variations in embryonic images. In the thesis work, we develop a localization framework based on the analysis of connected components of edge pixels in a scale space. We propose criteria to select optimal scales for embryonic localization. Furthermore, we propose a scale mapping strategy to compress the range of a scale space in order to improve the efficiency of the
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Partin, Michael. "Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567073723628721.

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Pradhan, Shameer Kumar. "Investigation of Event-Prediction in Time-Series Data : How to organize and process time-series data for event prediction?" Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19416.

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The thesis determines the type of deep learning algorithms to compare for a particular dataset that contains time-series data. The research method includes study of multiple literatures and conduction of 12 tests. It deals with the organization and processing of the data so as to prepare the data for prediction of an event in the time-series. It also includes the explanation of the algorithms selected. Similarly, it provides a detailed description of the steps taken for classification and prediction of the event. It includes the conduction of multiple tests for varied timeframe in order to com
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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training da
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van, Schaik Sebastiaan Johannes. "A framework for processing correlated probabilistic data." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:91aa418d-536e-472d-9089-39bef5f62e62.

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The amount of digitally-born data has surged in recent years. In many scenarios, this data is inherently uncertain (or: probabilistic), such as data originating from sensor networks, image and voice recognition, location detection, and automated web data extraction. Probabilistic data requires novel and different approaches to data mining and analysis, which explicitly account for the uncertainty and the correlations therein. This thesis introduces ENFrame: a framework for processing and mining correlated probabilistic data. Using this framework, it is possible to express both traditional and
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Vatandoust, Arman. "Machine Learning for Software Bug Categorization." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395253.

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The pursuit of flawless software is often an exhausting task for software developers. Code defects can range from soft issues to hard issues that lead to unforgiving consequences. DICE have their own system which automatically collects these defects which are grouped into buckets, however, this system suffers from the flaw of sometimes incorrectly grouping unrelated issues, and missing apparent duplicates. This time-consuming flaw puts excessive work for software developers and leads to wasted resources in the company. These flaws also impact the data quality of the system's defects tracking d
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Khan, Mohammed Salman. "A Topic Modeling approach for Code Clone Detection." UNF Digital Commons, 2019. https://digitalcommons.unf.edu/etd/874.

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In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation usi
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Hickman, Björn, and Victor Holmqvist. "Predict future software defects through machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301864.

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The thesis aims to investigate the implications of software defect predictions through machine learning on project management. In addition, the study aims to examine what features of a code base that are useful for making such predictions. The features examined are of both organisational and technical nature, indicated to correlate with the introductions of software defects by previous studies. The machine learning algorithms used in the study are Random forest, logistic regression and naive Bayes. The data was collected from an open source git-repository, VSCode, where the correct classificat
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Watson, Cody. "Deep Learning In Software Engineering." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444371.

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Software evolves and therefore requires an evolving field of Software Engineering. The evolution of software can be seen on an individual project level through the software life cycle, as well as on a collective level, as we study the trends and uses of software in the real world. As the needs and requirements of users change, so must software evolve to reflect those changes. This cycle is never ending and has led to continuous and rapid development of software projects. More importantly, it has put a great responsibility on software engineers, causing them to adopt practices and tools that al
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Martin, Andrew Philip. "Machine-assisted theorem-proving for software engineering." Thesis, University of Oxford, 1994. http://ora.ox.ac.uk/objects/uuid:728d3cee-1dfe-4186-a49f-52b33cbc6551.

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Husseini, Orabi Ahmed. "Multi-Modal Technology for User Interface Analysis including Mental State Detection and Eye Tracking Analysis." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36451.

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We present a set of easy-to-use methods and tools to analyze human attention, behaviour, and physiological responses. A potential application of our work is evaluating user interfaces being used in a natural manner. Our approach is designed to be scalable and to work remotely on regular personal computers using expensive and noninvasive equipment. The data sources our tool processes are nonintrusive, and captured from video; i.e. eye tracking, and facial expressions. For video data retrieval, we use a basic webcam. We investigate combinations of observation modalities to detect and extract af
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Jonsson, Nicklas. "Ways to use Machine Learning approaches for software development." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152812.

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With the rise of machine learning and in particular deep learning entering all different types of fields, including software development. It could be a bit hard to know where to begin to search for the tools when someone wants to use machine learning for a one’s problems. This thesis has looked at some available technologies of today for applying machine learning to one’s applications. This thesis has looked at some of the available cloud services, frame works, and libraries for machine learning and it presents three different implementation structures that can be used with these technologies
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Sharma, Oliver. "Detecting worm mutations using machine learning." Thesis, University of Glasgow, 2008. http://theses.gla.ac.uk/469/.

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Worms are malicious programs that spread over the Internet without human intervention. Since worms generally spread faster than humans can respond, the only viable defence is to automate their detection. Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean that new worms cannot be detected until the corresponding signatures are created, but that mutations of known worms will remain undetected because each mutation will usually have a different signature. The intuitive and seemingly most effective solu
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Phadke, Amit Ashok. "Predicting open-source software quality using statistical and machine learning techniques." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-11092004-105801.

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Forsberg, Fredrik, and Gonzalez Pierre Alvarez. "Unsupervised Machine Learning: An Investigation of Clustering Algorithms on a Small Dataset." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16300.

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Context: With the rising popularity of machine learning, looking at its shortcomings is valuable in seeing how well machine learning is applicable. Is it possible to apply the clustering with a small dataset? Objectives: This thesis consists of a literature study, a survey and an experiment. It investigates how two different unsupervised machine learning algorithms DBSCAN(Density-Based Spatial Clustering of Applications with Noise) and K-means run on a dataset gathered from a survey. Methods: Making a survey where we can see statistically what most people chose and apply clustering with the da
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Mayo, Quentin R. "Detection of Generalizable Clone Security Coding Bugs Using Graphs and Learning Algorithms." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404548/.

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This research methodology isolates coding properties and identifies the probability of security vulnerabilities using machine learning and historical data. Several approaches characterize the effectiveness of detecting security-related bugs that manifest as vulnerabilities, but none utilize vulnerability patch information. The main contribution of this research is a framework to analyze LLVM Intermediate Representation Code and merging core source code representations using source code properties. This research is beneficial because it allows source programs to be transformed into a graphical
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Kanneganti, Alekhya. "Using Ensemble Machine Learning Methods in Estimating Software Development Effort." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20691.

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Background: Software Development Effort Estimation is a process that focuses on estimating the required effort to develop a software project with a minimal budget. Estimating effort includes interpretation of required manpower, resources, time and schedule. Project managers are responsible for estimating the required effort. A model that can predict software development effort efficiently comes in hand and acts as a decision support system for the project managers to enhance the precision in estimating effort. Therefore, the context of this study is to increase the efficiency in estimating sof
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Kasianenko, Stanislav. "Predicting Software Defectiveness by Mining Software Repositories." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.

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One of the important aims of the continuous software development process is to localize and remove all existing program bugs as fast as possible. Such goal is highly related to software engineering and defectiveness estimation. Many big companies started to store source code in software repositories as the later grew in popularity. These repositories usually include static source code as well as detailed data for defects in software units. This allows analyzing all the data without interrupting programing process. The main problem of large, complex software is impossibility to control everythi
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Zam, Anton. "Evaluating Distributed Machine Learning using IoT Devices." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42388.

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Internet of things (IoT) blir bara större och större varje år och nya enheter läggs till hela tiden. Även om en stor del av dessa enheter är kontinuerligt använda finns det fortfarande väldigt många enheter som står inaktiva och sitter på oanvänd processorkraft som kan användas till att utföra maskininlärnings beräkningar. Det finns för nuvarande väldigt många metoder för att kombinera processorkraften av flera enheter för att utföra maskininlärnings uppgifter, dessa brukar kallas för distribuerade maskininlärnings metoder. huvudfokuset av detta arbetet är att utvärdera olika distribuerade mas
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45

Lloyd, Katherine L. "Machine learning stratification for oncology patient survival." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/107703/.

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Personalised medicine for cancer treatment promises benefits for patient survival and effective use of medical resources. This goal requires the development of predictive models for the identification and implementation of biomarkers for the prediction of patient survival given treatment options. This thesis addresses research questions in this area. The systematic review detailed in Chapter 2 investigates the literature concerning the prediction of resistance to chemotherapy for ovarian cancer patients using statistical methods and gene expression measurements. The range of models used by stu
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Bakhshi, Taimur. "User-centric traffic engineering in software defined networks." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/8202.

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Software defined networking (SDN) is a relatively new paradigm that decouples individual network elements from the control logic, offering real-time network programmability, translating high level policy abstractions into low level device configurations. The framework comprises of the data (forwarding) plane incorporating network devices, while the control logic and network services reside in the control and application planes respectively. Operators can optimize the network fabric to yield performance gains for individual applications and services utilizing flow metering and application-aware
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Niu, Fei. "Learning-based Software Testing using Symbolic Constraint Solving Methods." Licentiate thesis, KTH, Teoretisk datalogi, TCS, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-41932.

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Software testing remains one of the most important but expensive approaches to ensure high-quality software today. In order to reduce the cost of testing, over the last several decades, various techniques such as formal verification and inductive learning have been used for test automation in previous research. In this thesis, we present a specification-based black-box testing approach, learning-based testing (LBT), which is suitable for a wide range of systems, e.g. procedural and reactive systems. In the LBT architecture, given the requirement specification of a system under test (SUT), a la
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Allamanis, Miltiadis. "Learning natural coding conventions." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28791.

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Coding conventions are ubiquitous in software engineering practice. Maintaining a uniform coding style allows software development teams to communicate through code by making the code clear and, thus, readable and maintainable—two important properties of good code since developers spend the majority of their time maintaining software systems. This dissertation introduces a set of probabilistic machine learning models of source code that learn coding conventions directly from source code written in a mostly conventional style. This alleviates the coding convention enforcement problem, where con
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Andersson, Robin. "CAN-bus Multi-mixed IDS : A combinatory approach for intrusion detection in the controller area network of personal vehicles." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43450.

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With the digitalization and the ever more computerization of personal vehicles, new attack surfaces are introduced, challenging the security of the in-vehicle network. There is never such a thing as fully securing any computer system, nor learning all the methods of attack in order to prevent a break-in into a system. Instead, with sophisticated methods, we can focus on detecting and preventing attacks from being performed inside a system. The current state of the art of such methods, named intrusion detection systems (IDS), is divided into two main approaches. One approach makes its models ve
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Andersson, Robin. "Combining Anomaly- and Signaturebased Algorithms for IntrusionDetection in CAN-bus : A suggested approach for building precise and adaptiveintrusion detection systems to controller area networks." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43450.

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With the digitalization and the ever more computerization of personal vehicles, new attack surfaces are introduced, challenging the security of the in-vehicle network. There is never such a thing as fully securing any computer system, nor learning all the methods of attack in order to prevent a break-in into a system. Instead, with sophisticated methods, we can focus on detecting and preventing attacks from being performed inside a system. The current state of the art of such methods, named intrusion detection systems (IDS), is divided into two main approaches. One approach makes its models ve
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