Relevant bibliographies by topics / Computer software. Software engineering. Machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computer software. Software engineering. Machine learning'

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

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Journal articles on the topic "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 (May 30, 2021): 204–8. http://dx.doi.org/10.35940/ijrte.a5898.0510121.

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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 such interface descriptions are usually not available. So they have to be reverse engineered from existing event logs and source code. This costs a lot of time and does not scale very well. To improve the efficiency of this process, we present a passive learning technique for interface models inspired by process mining techniques. The approach is based on representing causal relations between events present in an event log and their timing information as a timed-causal graph. The graph is further processed and eventually transformed into a state machine and a set of timing constraints. Compared to other approaches in literature which focus on the general problem of inferring state-based behavior, we exploit patterns of client-server interactions in event logs.
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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 (June 15, 2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v2020.i7.6.694.

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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 are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.
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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 (June 15, 2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.694.

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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 are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.
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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 (January 2020): 67–95. http://dx.doi.org/10.1142/s0218194020500047.

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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 engineers in evaluating a software system based on well-defined sustainability metrics and measurements. We propose a novel approach that combines machine learning (ML) and software analysis methods. To simplify the application of the proposed approach, we present a semi-automated tool that supports engineers in assessing the sustainability achievement of a software system. The results of our study demonstrate that the proposed approach determines sustainability criteria and defines sustainability achievement in terms of a traceable matrix. Our theoretical evaluation and empirical study demonstrate that the proposed support tool can help engineers identify sustainability limitations in a particular feature of a software system. Our semi-automated tool can identify features that must be revised to enhance sustainability achievement.
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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 (December 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 pursued in a distinct prototype environment: design knowledge capture and knowledge reuse in KAPTUR (Knowledge Acquisition for Preservation of Tradeoffs and Underlying Rationales), and learning in LEARN (Learning Enhanced Automation of Reuse Engineering). Despite their prototype realization in different environments, the integration of these approaches into an overall KBSEE is a key goal of our work.
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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, Shahid Anwar, Shahreen Kasim, Arda Yunianta, and Tole Sutikno. "Adaboost-multilayer perceptron to predict the student’s performance in software engineering." Bulletin of Electrical Engineering and Informatics 8, no. 4 (December 1, 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 for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
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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 (June 2010): 125–36. http://dx.doi.org/10.1142/s1469026810002811.

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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 generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.
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Dissertations / Theses on the topic "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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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
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 specific to that project, and create a full machine learning pipeline. This pipeline will generate software test result predictions that the user can easily integrate with their existing testing processes. To do so, a novel test result collection system was built to collect the necessary data on which the prediction models could be trained. Test data was collected for Flask, a well-known Python open-source project. This data was then fed through SVDFeature, a matrix prediction model, to generate new test result predictions. Several methods for the test result prediction procedure were evaluated to demonstrate various methods of using the system.
by Bingfei Cao.
M. Eng.
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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.
Includes bibliographical references (p. 65-67).
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 of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
by Yuriy Brun.
M.Eng.
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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
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 metrics from source code files combined with metrics from their respective tests predicts faults with better prediction perfor- mance compared to using only metrics from the source code files. Methods. A literature review was conducted to identify inputs for an ex- periment. The experiment was applied on one repository from Ericsson to identify the best performing set of metrics. Results. The prediction performance results of three metric sets are pre- sented and compared with each other. Wilcoxon’s signed rank tests are performed on four different performance measures for each metric set and each machine learning algorithm to demonstrate significant differences of the results. Conclusions. We conclude that metrics from tests can be used to predict faults. However, the combination of source code metrics and test metrics do not outperform using only source code metrics. Moreover, we conclude that models built with metrics from the test metric set with minimal infor- mation of the source code can in fact predict faults in the source code.
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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 classification that are relevant for large scale data processing for surveillance (e.g. face recognition), where the aim is to augment decision making by reducing extremely large sets of data to a manageable level before displaying the selected subset of data to a human operator. In addition, an indicator for a weighted pairwise constraint is proposed to balance the contributions from different classes to the final optimisation, in order to better control the relative positions between the important data samples from either the same class (intraclass) or different classes (interclass). The effectiveness of the proposed method is evaluated through comparison with seven existing techniques for embedding learning, using four established databases of faces, consisting of various poses, lighting conditions and facial expressions, as well as two standard text datasets. The proposed method performs better than these existing techniques, especially for cases with small sets of training data samples. For fusion of low bit depth images, using low bit depth images instead of full images offers a number of advantages for aerial imaging with UAVs, where there is a limited transmission rate/bandwidth. For example, reducing the need for data transmission, removing superfluous details, and reducing computational loading of on-board platforms (especially for small or micro-scale UAVs). The main drawback of using low bit depth imagery is discarding image details of the scene. Fortunately, this can be reconstructed by fusing a sequence of related low bit depth images, which have been properly aligned. To reduce computational complexity and obtain a less distorted result, a similarity transformation is used to approximate the geometric alignment between two images of the same scene. The transformation is estimated using a phase correlation technique. It is shown that that the phase correlation method is capable of registering low bit depth images, without any modi�cation, or any pre and/or post-processing. For 3D reconstruction from 2D images, a method is proposed to deal with the dense reconstruction after a sparse reconstruction (i.e. a sparse 3D point cloud) has been created employing the structure from motion technique. Instead of generating a dense 3D point cloud, this proposed method forms a triangle by three points in the sparse point cloud, and then maps the corresponding components in the 2D images back to the point cloud. Compared to the existing methods that use a similar approach, this method reduces the computational cost. Instated of utilising every triangle in the 3D space to do the mapping from 2D to 3D, it uses a large triangle to replace a number of small triangles for flat and almost flat areas. Compared to the reconstruction result obtained by existing techniques that aim to generate a dense point cloud, the proposed method can achieve a better result while the computational cost is comparable.
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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, which cannot be captured using conventional types of sensors. It is shown that the proposed methodology is able to efficiently analyse and recognise complex activities arising in such tasks and also detect potential errors in their execution. To achieve this, a novel activity classification method comprising a feature selection stage based on the novel Key Actions Discovery method and a classification stage based on the combination of Random Forests and Hierarchical Hidden Markov Models is introduced. Experimental results captured in several scenarios arising from real-life applications, including a novel application to a bridge design problem, show that the proposed framework offers higher classification accuracy compared to current activity identification schemes.
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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-excitation instruments but not continuous-excitation instruments. This work improves the realism of synthesis of violin, viola, and cello sound by generating audio through modelling the physical behaviour of the instruments. A string's wave equation is decomposed into 40 modes of vibration, which can be acted upon by three forms of external force: A bow scraping against the string, a left-hand finger pressing down, and/or a right-hand finger plucking. The vibration of each string exerts force against the instrument bridge; these forces are summed and convolved with the instrument body impulse response to create the final audio output. In addition, right-hand haptic output is created from the force of the bow against the string. Physical constants from ten real instruments (five violins, two violas, and three cellos) were measured and used in these simulations. The physical modelling was implemented in a high-performance library capable of simulating audio on a desktop computer one hundred times faster than real-time. The program also generates animated video of the instruments being performed. To perform music with the physical models, a virtual musician interprets the musical score and generates actions which are then fed into the physical model. The resulting audio and haptic signals are examined with a support vector machine, which adjusts the bow force in order to establish and maintain a good timbre. This intelligent feedback control is trained with human input, but after the initial training is completed the virtual musician performs autonomously. A PID controller is used to adjust the position of the left-hand finger to correct any flaws in the pitch. Some performance parameters (initial bow force, force correction, and lifting factors) require an initial value for each string and musical dynamic; these are calibrated automatically using the previously-trained support vector machines. The timbre judgements are retained after each performance and are used to pre-emptively adjust bowing parameters to avoid or mitigate problematic timbre for future performances of the same music. The system is capable of playing sheet music with approximately the same ability level as a human music student after two years of training. Due to the number of instruments measured and the generality of the machine learning, music can be performed with ensembles of up to ten stringed instruments, each with a distinct timbre. This provides a baseline for future work in computer control and expressive music performance of virtual bowed string instruments.
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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. At each stage of the process currently used methods are investigated and evaluated. In cases where existingmethods do not produce satisfactory results improvedmethods have been suggested. In particular, this thesis presents a novel, automated,method for aligning LIDAR data to the stereo camera frame without the need for specialised alignment grids. For image segmentation a hybrid approach is presented, combining the strengths of both edge detection and mean-shift segmentation. For texture analysis the presented method uses GLCM metrics which allows texture information to be captured and summarised using only four feature descriptors compared to the 100’s produced by SURF descriptors. In addition to texture descriptors, the ìD information provided by the stereo system is also exploited. The segmented point cloud is used to determine orientation and curvature using polynomial surface fitting, a technique not yet applied to this application. Regarding classification methods a comprehensive study was carried out comparing the performance of the SVM and neural network algorithms for this particular application. The outcome shows that for this particular set of learning features the SVM classifiers offer slightly better performance in the context of image and depth based classification which was not made clear in existing literature. Finally a novel method of making unsupervised classifications is presented. Segments are automatically grouped into sub-classes which can then be mapped to more expressive super-classes as needed. Although the method in its current state does not yet match the performance of supervised methods it does produce usable classification results without the need for any training data. In addition, the method can be used to automatically sub-class classes with significant inter-class variation into more specialised groups prior to being used as training targets in a supervised method.
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Books on the topic "Computer software. Software engineering. Machine learning"

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

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European Working Session on Learning (1991 Porto, Portugal). Machine learning--EWSL-91: Proceedings. Berlin: 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. Berlin: 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. Berlin: Springer, 2007.

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Computational trust models and machine learning. Boca Raton: 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. Berlin: 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. Berlin: 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. Berlin: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Book chapters on the topic "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, 231–61. Boston, MA: 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, 174–79. Cham: 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, 155–83. Cham: 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, 30–37. Cham: 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, 246–58. Singapore: 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, 109–20. Cham: 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, 131–37. Berlin, Heidelberg: 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, 87–96. Cham: 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, 186–200. Berlin, Heidelberg: 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, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Predictive Techniques in Software Engineering." In Encyclopedia of Machine Learning, 782–89. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_661.

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Conference papers on the topic "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. Paris, France: 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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Reports on the topic "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, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23684.

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