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Journal articles on the topic 'Software Tools and Techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Jayasree, V., and M. D. Baby. "Scientometrics: Tools, Techniques and Software for Analysis." Indian Journal of Information Sources and Services 9, no. 2 (May 5, 2019): 116–21. http://dx.doi.org/10.51983/ijiss.2019.9.2.611.

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This paper aims to discuss the significance of e-resources on scientometrics study. Tools for scientometric analysis are listed out. Data collected from literature search and website of softwares. Citation tracking tools like Web of science, Scopus and Google Scholar citations, CiteseerX etc., are discussed. Various software tools for bibliometric analysis like Bibexcel, CiteSpace, Histcite, Pajek, Publish or Perish, Scholarometer, VOS viewer-tool for constructing and visualizing bibliometric networks, CitNet explorer – tool for visualizing and analysing citation networks of publications etc are discussed, The study concludes that combination of different software tools can be used for complete scientometric analysis and the familiarization of bibliometric software among students and researchers will help to promote research in scientometrics in a more productive method.

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2

Vandierendonck, Hans, and Tom Mens. "Techniques and Tools for Parallelizing Software." IEEE Software 29, no. 2 (March 2012): 22–25. http://dx.doi.org/10.1109/ms.2012.43.

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3

Buglione, Luigi, and Christof Ebert. "Estimation Tools and Techniques." IEEE Software 28, no. 3 (May 2011): 91–94. http://dx.doi.org/10.1109/ms.2011.55.

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4

Chaudhary, Sarika. "Latest Software Testing Tools and Techniques: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 538–40. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0138.

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5

Wainer, Gabriel A., and Olivier Dalle. "Software Tools, Techniques and Architectures for Computer Simulation." SIMULATION 86, no. 5-6 (May 2010): 267–69. http://dx.doi.org/10.1177/0037549710369877.

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6

Bazzana, G., R. Brigliadori, R. Cole, K. Kirkwood, and F. Seigneur. "Techniques and tools for software assessment and certification." Annual Review in Automatic Programming 16 (January 1992): 153–60. http://dx.doi.org/10.1016/0066-4138(92)90024-j.

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7

Buono, S., I. Gaponenko, R. Jones, A. Khodabandeh, L. Mapelli, G. Mornacchi, D. Prigent, et al. "Software engineering techniques and CASE tools in RD13." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 352, no. 1-2 (December 1994): 383–85. http://dx.doi.org/10.1016/0168-9002(94)91549-0.

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8

Feldman, Jerome A., and Thomas LeBlanc. "Coordinated computing: Tools and techniques for distributed software." Science of Computer Programming 5 (1985): 318–20. http://dx.doi.org/10.1016/0167-6423(85)90019-x.

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9

Srivastava, Nishi, Ujjwal Kumar, and Pawan Singh. "Software and Performance Testing Tools." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 1 (January 5, 2021): 1–12. http://dx.doi.org/10.54060/jieee/002.01.001.

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Software Testing may be a method, that involves, death penalty of a software system program/application and finding all errors or bugs therein program/application in order that the result is going to be a defect-free software system. Quality of any software system will solely be acknowledged through means that of testing (software testing). Through the advancement of technology round the world, there inflated the quantity of verification techniques and strategies to check the software system before it goes to production and astray to promote. Automation Testing has created its impact within the testing method. Now-a-days, most of the software system testing is finished with the automation tools that not solely lessens the quantity of individuals operating around that software system however additionally the errors which will be loose through the eyes of the tester. Automation take look acting contains test cases that make the work simple to capture totally different eventualities and store them. Therefore, software system automation testing method plays a significant role within the software system testing success. This study aims in knowing differing kinds of software system testing, software system testing techniques and tools and to match manual testing versus automation testing.

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10

Baker, Thar, Dhiya Al‐Jumeily, Zakaria Maamar, and Zahir Tari. "Semantic eSystems : Engineering methods, techniques, and tools." Software: Practice and Experience 51, no. 3 (January 11, 2021): 487–88. http://dx.doi.org/10.1002/spe.2952.

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11

Rizzoli, A. E., and W. J. Young. "Delivering environmental decision support systems: software tools and techniques." Environmental Modelling & Software 12, no. 2-3 (January 1997): 237–49. http://dx.doi.org/10.1016/s1364-8152(97)00016-9.

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12

NARDI, DANIELE, and MARCO TUCCI. "BUILDING TOOLS FOR SOFTWARE ENGINEERING WITH ARTIFICIAL INTELLIGENCE TECHNIQUES." Applied Artificial Intelligence 4, no. 4 (January 1990): 283–307. http://dx.doi.org/10.1080/08839519008927952.

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13

Fowler, Glenn S., James E. Humelsine, and Carl H. Olson. "Tools and Techniques for Building and Testing Software Systems." AT&T Technical Journal 71, no. 6 (November 12, 1992): 46–61. http://dx.doi.org/10.1002/j.1538-7305.1992.tb00514.x.

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14

Ranjan, Rajiv, Massimo Villari, Haiying Shen, Omer Rana, and Rajkumar Buyya. "Software tools and techniques for fog and edge computing." Software: Practice and Experience 50, no. 5 (May 2020): 473–75. http://dx.doi.org/10.1002/spe.2813.

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15

Reddy, G. Venkat, and Ahalya Chandrasekhar. "Tools and Techniques for testing of Flight Critical Software." Defence Science Journal 49, no. 4 (January 1, 1999): 317–22. http://dx.doi.org/10.14429/dsj.49.3844.

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16

Huisman, Marieke, and Julia Rubin. "Software quality tools and techniques presented in FASE’17." International Journal on Software Tools for Technology Transfer 20, no. 6 (September 3, 2018): 611–13. http://dx.doi.org/10.1007/s10009-018-0504-7.

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17

Afzal, Nadiya, and Mohd Sadim. "Analysis of advance software tools and techniques for software risk management and classification." Global Sci-Tech 10, no. 4 (2018): 187. http://dx.doi.org/10.5958/2455-7110.2018.00027.7.

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18

Kaur, Amandeep, Sandeep Sharma, and Munish Saini. "Code Clone Detection Using Machine Learning Techniques." International Journal of Open Source Software and Processes 11, no. 2 (April 2020): 49–75. http://dx.doi.org/10.4018/ijossp.2020040104.

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Code clone refers to code snippets that are copied and pasted with or without modifications. In recent years, traditional approaches for clone detection combine with other domains for better detection of a clone. This paper discusses the systematic literature review of machine learning techniques used in code clone detection. This study provides insights into various tools and techniques developed for clone detection by implementing machine learning approaches and how effectively those tools and techniques to identify clones. The authors perform a systematic literature review on studies selected from popular computer science-related digital online databases from January 2004 to January 2020. The software system and datasets used for analyzing tools and techniques are mentioned. A neural network machine learning technique is primarily used for the identification of the clone. Clone detection based on a program dependency graph must be explored in the future because it carries semantic information of code fragments.

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19

Beyer, Dirk, and Marieke Huisman. "Tools for the construction and analysis of systems." International Journal on Software Tools for Technology Transfer 22, no. 6 (July 13, 2020): 685–87. http://dx.doi.org/10.1007/s10009-020-00581-0.

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Abstract In order to develop reliable software and systems, we depend on practical techniques for the construction and analysis of such software and systems. This special issue of Software Tools for Technology Transfer presents various tool-supported techniques that can help with the construction and analysis of such reliable software and systems. The papers in this special issue are extended versions of selected conference papers from the proceedings of the 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018).

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20

McNamee, Dylan, Jonathan Walpole, Calton Pu, Crispin Cowan, Charles Krasic, Ashvin Goel, Perry Wagle, Charles Consel, Gilles Muller, and Renauld Marlet. "Specialization tools and techniques for systematic optimization of system software." ACM Transactions on Computer Systems 19, no. 2 (May 2001): 217–51. http://dx.doi.org/10.1145/377769.377778.

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21

Cullyer, W. J., and N. Storey. "Tools and techniques for the testing of safety-critical software." Computing & Control Engineering Journal 5, no. 5 (October 1, 1994): 239–44. http://dx.doi.org/10.1049/cce:19940509.

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22

Larson, Eric. "An undergraduate course on software bug detection tools and techniques." ACM SIGCSE Bulletin 38, no. 1 (March 31, 2006): 249–53. http://dx.doi.org/10.1145/1124706.1121418.

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23

Al-Zaghameem, Abdullah O. "Evaluating the Role of Software Visualization Techniques as Assistant Tools in Software Reverse Engineering." International Journal of Software Engineering and Its Applications 12, no. 3 (October 31, 2018): 39–50. http://dx.doi.org/10.14257/ijseia.2018.12.3.04.

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24

GÓMEZ-PÉREZ, ASUNCIÓN, and DAVID MANZANO-MACHO. "An overview of methods and tools for ontology learning from texts." Knowledge Engineering Review 19, no. 3 (September 2004): 187–212. http://dx.doi.org/10.1017/s0269888905000251.

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Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.

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25

V. Gayetri Devi, S., C. Nalini, and N. Kumar. "An efficient software verification using multi-layered software verification tool." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 454. http://dx.doi.org/10.14419/ijet.v7i2.21.12465.

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Rapid advancements in Software Verification and Validation have been critical in the wide development of tools and techniques to identify potential Concurrent bugs and hence verify the software correctness. A concurrent program has multiple processes and shared objects. Each process is a sequential program and they use the shared objects for communication for completion of a task. The primary objective of this survey is retrospective review of different tools and methods used for the verification of real-time concurrent software. This paper describes the proposed tool ‘F-JAVA’ for multithreaded Java codebases in contrast with existing ‘FRAMA-C’ platform, which is dedicated to real-time concurrent C software analysis. The proposed system is comprised of three layers, namely Programming rules generation stage, Verification stage with Particle Swarm Optimization (PSO) algorithm, and Performance measurement stage. It aims to address some of the challenges in the verification process such as larger programs, long execution times, and false alarms or bugs, and platform independent code verification

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26

Sæter, Tore. "Software techniques for integrating text and graphics in VLSI CAD tools." Microprocessing and Microprogramming 24, no. 1-5 (August 1988): 455–60. http://dx.doi.org/10.1016/0165-6074(88)90096-8.

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27

Rose, Kenneth H., and C. Eric Kirkland. "Book Review: Mastering Software Project Management: Best Practices, Tools and Techniques." Project Management Journal 42, no. 6 (December 2011): 92. http://dx.doi.org/10.1002/pmj.20276.

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28

Abdurachmanov, David, Peter Elmer, Giulio Eulisse, Robert Knight, Tapio Niemi, Jukka K. Nurminen, Filip Nyback, Gonçalo Pestana, Zhonghong Ou, and Kashif Khan. "Techniques and tools for measuring energy efficiency of scientific software applications." Journal of Physics: Conference Series 608 (May 22, 2015): 012032. http://dx.doi.org/10.1088/1742-6596/608/1/012032.

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29

Wang, Lizhe, Rajiv Ranjan, Joanna Kołodziej, Albert Zomaya, and Leila Alem. "Software Tools and Techniques for Big Data Computing in Healthcare Clouds." Future Generation Computer Systems 43-44 (February 2015): 38–39. http://dx.doi.org/10.1016/j.future.2014.11.001.

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30

Dahl, Susan G., Laurel Allender, Troy Kelley, and Richard Adkins. "Transitioning Software to the Windows Environment - Challenges and Innovations." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 39, no. 18 (October 1995): 1224–27. http://dx.doi.org/10.1177/154193129503901816.

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Over the past ten years, the U.S. Army Research Laboratory (ARL) Human Research and Engineering Directorate (HRED) has developed tools and techniques to support Manpower and Personnel Integration (MANPRINT). Most notably, a set of tools was developed in the DOS environment that has become known as the Hardware vs. Manpower (HARDMAN) III tools. These software tools provide an analytical basis to address the ways in which the Army's manpower, personnel, and training elements are affected by a new system. During the last two years, ARL HRED has begun an effort to improve the capabilities of this tool set by moving them into the MicroSoft Windows™ environment. This paper describes the process through which this complex DOS tool set was redesigned to provide a better functional capability as well as to take advantage of the graphical user interface provided by this environment.

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31

Bright, David S. "Software Tools for Examination of Microanalytical Images." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 2 (August 12, 1990): 116–17. http://dx.doi.org/10.1017/s042482010013417x.

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Image processing for enhancement and interpretation is a powerful tool for microscopy and microanalysis. Digital images are arrays of picture elements or pixels each having a coordinate (location) and a value. Two dimensional arrays with single intensity value (monochrome) or triple intensity values (color) pixels are in common use. Software tools and techniques are now available for desk top computers at reasonable cost that allow visualization of higher dimensional arrays and multivalued pixels. The following examples illustrate the application of these tools to microanalysis.Short image sequences (movies) are useful for showing dynamic effects such as the drift of an electron microscope stage with time or the interior of a sample eroded by sputtering on an ion microscope.The values of the pixels of registered images or x-ray maps can be accumulated in a multidimensional histogram (Concentration Histogram Image or CHI). The number of registered maps determines the dimensionality of the histogram.

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32

Daly, P. "Review: XML: Principles, Tools and Techniques." Computer Bulletin 40, no. 5 (September 1, 1998): 31. http://dx.doi.org/10.1093/combul/40.5.31-d.

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33

Rashidinia, Anoushirvan, S. Gavrilenko, M. Pochebut, and O. Sytnikova. "SOFTWARE SECURITY OVERVIEW." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 54 (April 11, 2019): 55–59. http://dx.doi.org/10.26906/sunz.2019.2.055.

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The article analyzes the main threats and problems of software protection. Methods for protecting information, their advantages and disadvantages are considered, and the possibility of using existing tools to protect software is studied. The possibility of improving and using a number of software protection methods against active fraud attacks was brought. Type of attacks exists and why protection is necessary was specified. Furthermore, we discussed several states of the art protection techniques which can be used in software to protect against analysis and tampering attacks. Analyzed such methods: Client-Server Solutions, Code Encryption, Code Diversity, Code Obfuscation, White-Box Cryptography, Tamper Resistant Software, Software Guards, Oblivious Hashing. Although we considered all these possible techniques separately, it is possible to combine these techniques into one solution.

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34

Polo, Macario, Sergio Tendero, and Mario Piattini. "Integrating techniques and tools for testing automation." Software Testing, Verification and Reliability 17, no. 1 (2007): 3–39. http://dx.doi.org/10.1002/stvr.348.

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35

Dearnley, P. "Information systems develpment: methodologies, techniques and tools." Information and Software Technology 31, no. 5 (June 1989): 285. http://dx.doi.org/10.1016/0950-5849(89)90013-x.

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36

Wang, Yingzi, Muhammad Nazir Jan, Sisi Chu, and Yue Zhu. "Use of Big Data Tools and Industrial Internet of Things: An Overview." Scientific Programming 2020 (October 21, 2020): 1–10. http://dx.doi.org/10.1155/2020/8810634.

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Big data is ever playing an important role in the industry as well as many other organizations. With the passage of time, the volume of data is increasing. This increase will create huge bulk of data which needs proper tools and techniques to handle its management and organization. Different techniques and tools are being used to properly handle the management of data. A detailed report of these techniques and tools is needed which will help researchers to easily identify a tool for their data and take help to easily manage the data, organize the data, and extract meaningful information from it. The proposed study is an endeavour toward summarizing and identifying the tools and techniques for big data used in Industrial Internet of Things. This report will certainly help researchers and practitioners to easily use the tools and techniques for their need in an effective way.

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37

Ponomarev, Alexey, and Hitesh S. Nalamwar. "Software Traceability – A Key to Improve Software Evolution." Key Engineering Materials 685 (February 2016): 881–85. http://dx.doi.org/10.4028/www.scientific.net/kem.685.881.

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Software traceability is an important part in software development that is getting more and more attention nowadays from organizations and researchers. The paper outlines the importance, different methods and techniques of software traceability. It also explains the need of automating traceability, problems and drawbacks of existing traceability tools, the ongoing challenges facing implementation of traceability in software development life cycle, and finally the paper discusses whether software traceability should be mandated as a key to improve software evolution

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38

Perduta, Anna, and Roman Putanowicz. "Tools and techniques for building models for isogeometric analysis." Advances in Engineering Software 127 (January 2019): 70–81. http://dx.doi.org/10.1016/j.advengsoft.2018.10.008.

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39

Garcia-Moreno, Adrian, and Pedro Carmona-Saez. "Computational Methods and Software Tools for Functional Analysis of miRNA Data." Biomolecules 10, no. 9 (August 28, 2020): 1252. http://dx.doi.org/10.3390/biom10091252.

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miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

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40

Vega-Rodríguez, Miguel A., Juan M. Sánchez-Pérez, and Juan A. Gómez-Pulido. "Advances in FPGA tools and techniques." Microprocessors and Microsystems 29, no. 2-3 (April 2005): 47–49. http://dx.doi.org/10.1016/j.micpro.2004.06.003.

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41

Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (July 2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.

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42

Vasylchenko, A., Y. Schols, W. De Raedt, and G. A. E. Vandenbosch. "Quality Assessment of Computational Techniques and Software Tools for Planar-Antenna Analysis." IEEE Antennas and Propagation Magazine 51, no. 1 (February 2009): 23–38. http://dx.doi.org/10.1109/map.2009.4939017.

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43

de Jesus, T. O., and M. S. Soares. "A Multi-Criteria Analysis of Techniques and Tools for Tracing Software Requirements." IEEE Latin America Transactions 15, no. 5 (May 2017): 922–27. http://dx.doi.org/10.1109/tla.2017.7910207.

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44

Russo, Alessandra, and Andy Schürr. "Model-based software quality assurance tools and techniques presented at FASE 2018." International Journal on Software Tools for Technology Transfer 22, no. 1 (October 11, 2019): 1–2. http://dx.doi.org/10.1007/s10009-019-00541-3.

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45

Asha, N., and Prasanna Mani. "Literature review on software testing techniques and test suite reduction frameworks/tools." International Journal of Advanced Intelligence Paradigms 19, no. 3/4 (2021): 438. http://dx.doi.org/10.1504/ijaip.2021.116370.

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46

Xun, Jing Bin, Gang Wang, Zhi Feng Qiao, Yan Yu Ding, Xiang Gui Ling, and Zhi Yuan Tang. "The Development of DNC Transfer Software Used in CNC Machine Tools." Applied Mechanics and Materials 141 (November 2011): 550–53. http://dx.doi.org/10.4028/www.scientific.net/amm.141.550.

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The network manufacturing technology of CNC machine tools can greatly increase their manufacturing efficiency, reduce production costs, enhance competitiveness and has significant practical significance. The paper studied the communication techniques used in CNC machine tool, took the programs developed in the practical application for example, described the process of preparing the application serial communication, completed the overall design of the structure of DNC system.

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47

HURLEY, W. DAVID. "A TOOL FOR CONSTRUCTING INTERACTIVE SOFTWARE." International Journal of Software Engineering and Knowledge Engineering 01, no. 01 (March 1991): 75–98. http://dx.doi.org/10.1142/s0218194091000081.

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The Tailor is a research tool for refining and evaluating a design representation for software systems that have a high degree of textual and graphical interaction. The design representation incorporates both software engineering and knowledge engineering techniques, providing a set of conventions for describing software design information and software construction knowledge. The Tailor demonstrates that the design representation, when embedded in a productivity tool, overcomes limitations of current generation user interface tools. For example, the Tailor has the capability to foresee construction difficulties arising from earlier design decisions. It uses this capability to provide active guidance in producing a software design that avoids problems otherwise overlooked during software specification and design.

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48

Ominsky, Mark, Fredrick A. Muckler, Colin G. Drury, Michael L. Frazier, Barry L. Berson, Edwin R. Smootz, and D. Todd Jones. "Testing and Evaluation Symposium: Tools, Techniques and Facilities." Proceedings of the Human Factors Society Annual Meeting 30, no. 12 (September 1986): 1142–45. http://dx.doi.org/10.1177/154193128603001201.

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This symposium is the first sponsored by the newly formed Test and Evaluation Technical Group. The scope of interest was defined by a broad definition statement that included “any formally scheduled tests of personnel performance utilizing mockups, developmental and prototype equipment and software during system development” or follow-up testing. The objective of this Symposium is to draw together people from various employment situations, who are performing testing and evaluation on a daily basis, to discuss the tools, techniques and facilities associated with doing that part of their job. The purpose is to focus on the process of doing the job, rather than on the specific results of testing.

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49

Drakoulogkonas, Panagiotis, and Dimitris Apostolou. "On the Selection of Process Mining Tools." Electronics 10, no. 4 (February 11, 2021): 451. http://dx.doi.org/10.3390/electronics10040451.

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Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent years and new process mining software tools, both academic and commercial, have been developed. This paper provides a survey of process mining software tools. It identifies and describes criteria that can be useful for comparing the tools. Furthermore, it introduces a multi-criteria methodology that can be used for the comparative analysis of process mining software tools. The methodology is based on three methods, namely ontology, decision tree, and Analytic Hierarchy Process (AHP), that can be used to help users decide which software tool best suits their needs.

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50

Khan, M. N. A., A. M. Mirza, and I. Saleem. "Software Risk Analysis with the use of Classification Techniques: A Review." Engineering, Technology & Applied Science Research 10, no. 3 (June 7, 2020): 5678–82. http://dx.doi.org/10.48084/etasr.3440.

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Risk analysis and management is a critical aspect of the software development process. Various risks are associated with every phase of the software development lifecycle. The early identification of risks in each phase of software development coupled with mitigating plans can help to reduce the cost of the product and increase software quality. This study aims to explore various tools and techniques used in the literature of analyzing and managing risks. Most risk analysis techniques have been applied in the requirement analysis phase, so there is a scarcity of tools supporting automated risk analysis. Accommodating various types of risk factors to predict the software risks reduces the accuracy of the classifier.

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