Bibliografie tematiche / Computer software. Software engineering. Machine learning

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Letteratura scientifica selezionata sul tema "Computer software. Software engineering. Machine learning"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 4 febbraio 2022

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Articoli di riviste sul tema "Computer software. Software engineering. Machine learning"

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Hussain*, Mandi Akif, Revoori Veeharika Reddy, Kedharnath Nagella, and Vidya S. "Software Defect Estimation using Machine Learning Algorithms." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 204–8. http://dx.doi.org/10.35940/ijrte.a5898.0510121.

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Abstract (sommario):
Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.
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Bera, Debjyoti, Mathijs Schuts, Jozef Hooman, and Ivan Kurtev. "Reverse engineering models of software interfaces." Computer Science and Information Systems 18, no. 3 (2021): 657–86. http://dx.doi.org/10.2298/csis200131013b.

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Cyber-physical systems consist of many hardware and software components. Over the lifetime of these systems their components are often replaced or updated. To avoid integration problems, formal specifications of component interface behavior are crucial. Such a formal specification captures not only the set of provided operations but also the order of using them and the constraints on their timing behavior. Usually the order of operations are expressed in terms of a state machine. For new components such a formal specification can be derived from requirements. However, for legacy components suc
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Chung, Chih-Ko, and Pi-Chung Wang. "Version-Wide Software Birthmark via Machine Learning." IEEE Access 9 (2021): 110811–25. http://dx.doi.org/10.1109/access.2021.3103186.

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Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v2020.i7.6.694.

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Abstract (sommario):
Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries.
 Software engineers
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Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.694.

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Abstract (sommario):
Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries.
 Software engineers
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Saputri, Theresia Ratih Dewi, and Seok-Won Lee. "Software Analysis Method for Assessing Software Sustainability." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (2020): 67–95. http://dx.doi.org/10.1142/s0218194020500047.

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Abstract (sommario):
Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engin
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BAILIN, SIDNEY C., ROBERT H. GATTIS, and WALT TRUSZKOWSKI. "A LEARNING-BASED SOFTWARE ENGINEERING ENVIRONMENT FOR REUSING DESIGN KNOWLEDGE." International Journal of Software Engineering and Knowledge Engineering 01, no. 04 (1991): 351–71. http://dx.doi.org/10.1142/s0218194091000251.

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As part of the NASA/Goddard Code 522.3 research program in software engineering, a Knowledge-Based Software Engineering Environment (KBSEE) is being developed. The KBSEE will support a comprehensive artifact-reuse capability and will incorporate knowledge-based concepts such as machine learning and design knowledge capture. The distinguishing features of this work are that it is a systematic approach to the reuse of knowledge, not just of products, and it implements learning as an explicitly supported function in a software engineering environment. Each of these objectives is currently being p
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Siewruk, Grzegorz, and Wojciech Mazurczyk. "Context-Aware Software Vulnerability Classification Using Machine Learning." IEEE Access 9 (2021): 88852–67. http://dx.doi.org/10.1109/access.2021.3075385.

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Firdaus Zainal Abidin, Ahmad, Mohd Faaizie Darmawan, Mohd Zamri Osman, et al. "Adaboost-multilayer perceptron to predict the student’s performance in software engineering." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1556–62. http://dx.doi.org/10.11591/eei.v8i4.1432.

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Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction fo
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AZAR, DANIELLE. "A GENETIC ALGORITHM FOR IMPROVING ACCURACY OF SOFTWARE QUALITY PREDICTIVE MODELS: A SEARCH-BASED SOFTWARE ENGINEERING APPROACH." International Journal of Computational Intelligence and Applications 09, no. 02 (2010): 125–36. http://dx.doi.org/10.1142/s1469026810002811.

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Abstract (sommario):
In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to general
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Tesi sul tema "Computer software. Software engineering. Machine learning"

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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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Abstract (sommario):
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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Abstract (sommario):
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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Abstract (sommario):
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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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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Libri sul tema "Computer software. Software engineering. Machine learning"

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Daniele, Gunetti, ed. Inductive logic programming: From machine learning to software engineering. MIT Press, 1996.

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European Working Session on Learning (1991 Porto, Portugal). Machine learning--EWSL-91: Proceedings. Springer-Verlag, 1991.

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European Working Session on Learning (1991 Porto, Portugal). Machine learning--EWSL-91: European Working Session on Learning, Porto, Portugal, March 6-8, 1991 : proceedings. Springer-Verlag, 1991.

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S, Chen Peter P., Wong Leah Y, and International Conference on Conceptual Modeling (25th : 2006 : Tucson, Ariz.), eds. Active conceptual modeling of learning: Next generation learning-base system development. Springer, 2007.

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Computational trust models and machine learning. Taylor & Francis, 2014.

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ALT 2004 (2004 Padua, Italy). Algorithmic learning theory: 15th international conference, ALT 2004, Padova, Italy, October 2-5, 2004 : proceedings. Springer, 2004.

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P, O'Hare G. M., ed. Engineering societies in the agents world VII: 7th international workshop, ESAW 2006, Dublin, Ireland, September 6-8, 2006 : revised selected and invited papers. Springer, 2007.

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International, Conference on Artificial Neural Networks and Genetic Algorithms (2007 Warsaw Poland). Adaptive and natural computing algorithms: 8th international conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007 : proceedings. Springer, 2007.

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David, Hutchison. Engineering Societies in the Agents World IX: 9th International Workshop, ESAW 2008, Saint-Etienne, France, September 24-26, 2008, Revised Selected Papers. Springer Berlin Heidelberg, 2009.

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Stützle, Thomas. Learning and Intelligent Optimization: Third International Conference, LION 3, Trento, Italy, January 14-18, 2009. Selected Papers. Springer-Verlag Berlin Heidelberg, 2009.

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Capitoli di libri sul tema "Computer software. Software engineering. Machine learning"

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Kodratoff, Y. "Ten Years of Advances in Machine Learning." In Computer Systems and Software Engineering. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3506-5_9.

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Nakajima, Shin. "Generalized Oracle for Testing Machine Learning Computer Programs." In Software Engineering and Formal Methods. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74781-1_13.

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Subbiah, Uma, Muthu Ramachandran, and Zaigham Mahmood. "Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure." In Computer Communications and Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33624-0_7.

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Diako, Doffou Jerome, Odilon Yapo M. Achiepo, and Edoete Patrice Mensah. "Analysis of Software Vulnerabilities Using Machine Learning Techniques." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41593-8_3.

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Alloghani, Mohamed, Dhiya Al-Jumeily, Thar Baker, Abir Hussain, Jamila Mustafina, and Ahmed J. Aljaaf. "Applications of Machine Learning Techniques for Software Engineering Learning and Early Prediction of Students’ Performance." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3441-2_19.

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Cruz, Henry, Tatiana Gualotuña, María Pinillos, Diego Marcillo, Santiago Jácome, and Efraín R. Fonseca C. "Machine Learning and Color Treatment for the Forest Fire and Smoke Detection Systems and Algorithms, a Recent Literature Review." In Artificial Intelligence, Computer and Software Engineering Advances. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68080-0_8.

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Amamra, Abdelfattah, Chamseddine Talhi, Jean-Marc Robert, and Martin Hamiche. "Enhancing Smartphone Malware Detection Performance by Applying Machine Learning Hybrid Classifiers." In Computer Applications for Software Engineering, Disaster Recovery, and Business Continuity. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35267-6_17.

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Stouky, Ali, Btissam Jaoujane, Rachid Daoudi, and Habiba Chaoui. "Improving Software Automation Testing Using Jenkins, and Machine Learning Under Big Data." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98752-1_10.

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Rivest, Ronald L., and Werner Remmele. "Machine Learning." In Angewandte Informatik und Software / Applied Computer Science and Software. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-93501-5_16.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Predictive Techniques in Software Engineering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_661.

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Atti di convegni sul tema "Computer software. Software engineering. Machine learning"

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Yalciner, Burcu, and Merve Ozdes. "Software Defect Estimation Using Machine Learning Algorithms." In 2019 4th International Conference on Computer Science and Engineering (UBMK). IEEE, 2019. http://dx.doi.org/10.1109/ubmk.2019.8907149.

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Nakajima, Shin, and Hai Ngoc Bui. "Dataset Coverage for Testing Machine Learning Computer Programs." In 2016 23rd Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2016. http://dx.doi.org/10.1109/apsec.2016.049.

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Gensheng, Hu, and Liang Dong. "Multi-output Support Vector Machine Regression and Its Online Learning." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1024.

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Boriratrit, Sarunyoo, Sirapat Chiewchanwattana, Khamron Sunat, Pakarat Musikawan, and Punyaphol Horata. "Improvement flower pollination extreme learning machine based on meta-learning." In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2016. http://dx.doi.org/10.1109/jcsse.2016.7748871.

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Maneerat, Nakarin, and Pomsiri Muenchaisri. "Bad-smell prediction from software design model using machine learning techniques." In 2011 International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2011. http://dx.doi.org/10.1109/jcsse.2011.5930143.

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Kyaw, Aye Thandar, May Zin Oo, and Chit Su Khin. "Machine-Learning Based DDOS Attack Classifier in Software Defined Network." In 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2020. http://dx.doi.org/10.1109/ecti-con49241.2020.9158230.

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Boriratrit, Sarunyoo, Sirapat Chiewchanwattana, Khamron Sunat, Pakarat Musikawan, and Punyaphol Horata. "Harmonic extreme learning machine for data clustering." In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2016. http://dx.doi.org/10.1109/jcsse.2016.7748872.

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Augustijn, Ellen-Wien, Shaheen A. Abdulkareem, Mohammed Hikmat Sadiq, and Ali A. Albabawat. "Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing." In 2020 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2020. http://dx.doi.org/10.1109/csase48920.2020.9142117.

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Zhu, Qiuxi, Xiaodong Li, and Weijie Mao. "Image super-resolution representation via image patches based on extreme learning machine." In 2013 International Conference on Software Engineering and Computer Science. Atlantis Press, 2013. http://dx.doi.org/10.2991/icsecs-13.2013.61.

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Vinitnantharat, Napas, Narit Inchan, Thatthai Sakkumjorn, Kitsada Doungjitjaroen, and Chukiat Worasucheep. "Quantitative Trading Machine Learning Using Differential Evolution Algorithm." In 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2019. http://dx.doi.org/10.1109/jcsse.2019.8864226.

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Rapporti di organizzazioni sul tema "Computer software. Software engineering. Machine learning"

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Chichikin, V. A. The distance learning course "System software", direction podgotov 09.03.01 "Informatics and computer engineering". OFERNIO, 2018. http://dx.doi.org/10.12731/ofernio.2018.23684.

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