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Journal articles on the topic 'Software Measurement'

To see the other types of publications on this topic, follow the link: Software Measurement.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

TAKAMASU, Kiyoshi. "Roles of Software for Profile Measurement. Software for Profile Measurements." Journal of the Japan Society for Precision Engineering 61, no. 8 (1995): 1049–53. http://dx.doi.org/10.2493/jjspe.61.1049.

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2

Quentin, G. "Review: Software Measurement." Computer Bulletin 39, no. 3 (June 1, 1997): 28. http://dx.doi.org/10.1093/combul/39.3.28.

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3

Kearney, Joseph P., Robert L. Sedlmeyer, William B. Thompson, Michael A. Gray, and Michael A. Adler. "Software complexity measurement." Communications of the ACM 29, no. 11 (November 1986): 1044–50. http://dx.doi.org/10.1145/7538.7540.

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4

Jørgensen, M. "Software quality measurement." Advances in Engineering Software 30, no. 12 (December 1999): 907–12. http://dx.doi.org/10.1016/s0965-9978(99)00015-0.

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5

Everett, W. W. "Software reliability measurement." IEEE Journal on Selected Areas in Communications 8, no. 2 (1990): 247–52. http://dx.doi.org/10.1109/49.46878.

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6

Barford, Lee. "Software in measurement and measurement in software [Life after Graduation]." IEEE Instrumentation & Measurement Magazine 18, no. 3 (June 2015): 40–41. http://dx.doi.org/10.1109/mim.2015.7108398.

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7

KIYONO, Satoshi. "Roles of Software for Profile Measurement. Profile Measurement Using Software Datums." Journal of the Japan Society for Precision Engineering 61, no. 8 (1995): 1059–63. http://dx.doi.org/10.2493/jjspe.61.1059.

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8

Hai-Liang, Jian, and Wang Chong-Wen. "Web-Oriented Software Reliability Measurement Model and Application." International Journal of Engineering and Technology 4, no. 4 (2012): 358–61. http://dx.doi.org/10.7763/ijet.2012.v4.383.

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9

Offen, R. J., and R. Jeffery. "Establishing software measurement programs." IEEE Software 14, no. 2 (1997): 45–53. http://dx.doi.org/10.1109/52.582974.

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10

Kitchenham, B. A., R. T. Hughes, and S. G. Linkman. "Modeling software measurement data." IEEE Transactions on Software Engineering 27, no. 9 (2001): 788–804. http://dx.doi.org/10.1109/32.950316.

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11

Kemerer, Chris F. "Software development productivity measurement." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 17, no. 4 (July 1986): 41. http://dx.doi.org/10.1145/1113523.1113533.

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12

Smith, Tony. "Image Measurement Analysis Software." Anti-Corrosion Methods and Materials 41, no. 4 (April 1994): 19. http://dx.doi.org/10.1108/eb007345.

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13

Norin, Veniamin. "Statistical processing of multiple measurements results." E3S Web of Conferences 389 (2023): 07004. http://dx.doi.org/10.1051/e3sconf/202338907004.

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The product quality is determined by improvements of equipment, technologies, and production arrangement, which directly depends on the accuracy of measurement information. To achieve high measurement accuracy, it is reasonable to automate measurement processes. In case of automation, some functions are performed by equipment computer programs. The processing of direct comprehensive measurements is a complicated process including multiple algorithms of computation and various hypothesis tests. Taking into account the complexity and duration of statistical processing of results of multiple measurements, this paper is intended to develop a software measurement suite to process direct multiple measurements. The software measurement suite is a number of tools and software programs operating together to accomplish the tasks related with acquisition of required parameters and measurement results. To achieve this goal, it was required to address the following issues: implementation of the advanced measurement technologies into the developed software measurement suite; using wireless transmission of observation findings; ensuring compatibility of the developed software with the proprietary software of the used measurement instruments; improving the quality of measurements; improving the quality of processing measurement results by minimising the human factor effects on the processing quality; minimising the time spend for processing of the obtained measurement results.

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14

K.P, Srinivasan. "Unique Fundamentals of Software Measurement and Software Metrics in Software Engineering." International Journal of Computer Science and Information Technology 7, no. 4 (August 31, 2015): 29–43. http://dx.doi.org/10.5121/ijcsit.2015.7403.

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15

Julias Ceasor, A., M. Sundaram, and S. Gregory Thaddeus. "Measurement-Based Software Engineering Education." International Journal of Business Intelligents 001, no. 002 (December 10, 2012): 43–46. http://dx.doi.org/10.20894/ijbi.105.001.002.005.

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16

Shen, Wen-Hsiang, Nien-Lin Hsueh, and Peng-Hua Chu. "Measurement-based Software Process Modeling." Journal of Software Engineering 5, no. 1 (December 15, 2010): 20–37. http://dx.doi.org/10.3923/jse.2011.20.37.

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17

Zhou, Yang, Omid Alipourfard, Minlan Yu, and Tong Yang. "Accelerating network measurement in software." ACM SIGCOMM Computer Communication Review 48, no. 3 (September 7, 2018): 2–12. http://dx.doi.org/10.1145/3276799.3276800.

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18

Clark, B. "Eight secrets of software measurement." IEEE Software 19, no. 5 (September 2002): 12–14. http://dx.doi.org/10.1109/ms.2002.1032844.

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19

Rifkin, Stan. "Guest Editor's Introduction: Software Measurement." IEEE Software 26, no. 3 (May 2009): 70. http://dx.doi.org/10.1109/ms.2009.69.

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20

Bradley, J. S., and R. E. Halliwell. "New room acoustics measurement software." Journal of the Acoustical Society of America 80, S1 (December 1986): S39. http://dx.doi.org/10.1121/1.2023780.

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21

Pfleeger, S. L., R. Jeffery, B. Curtis, and B. Kitchenham. "Status report on software measurement." IEEE Software 14, no. 2 (1997): 33–43. http://dx.doi.org/10.1109/52.582973.

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22

Briand, L. C., S. Morasca, and V. R. Basili. "Property-based software engineering measurement." IEEE Transactions on Software Engineering 22, no. 1 (1996): 68–86. http://dx.doi.org/10.1109/32.481535.

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23

Rogers, Laurence. "Measurement software—tools for thinking." Electronic Systems News 1987, no. 1 (1987): 11. http://dx.doi.org/10.1049/esn.1987.0004.

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24

Morisio, M. "Measurement processes are software, too." Journal of Systems and Software 49, no. 1 (December 1999): 17–31. http://dx.doi.org/10.1016/s0164-1212(99)00063-1.

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25

Munson, John C. "Software measurement: Problems and practice." Annals of Software Engineering 1, no. 1 (December 1995): 255–85. http://dx.doi.org/10.1007/bf02249053.

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26

Hernández-López, Adrián, Ricardo Colomo-Palacios, Pedro Soto-Acosta, and Cristina Casado Lumberas. "Productivity Measurement in Software Engineering." International Journal of Information Technologies and Systems Approach 8, no. 1 (January 2015): 46–68. http://dx.doi.org/10.4018/ijitsa.2015010103.

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Productivity measurement is constructed by the measure of tree categories of elements: inputs, outputs and factors. This concept, which started being used in the manufacturing industry, has been also a research topic within Software Engineering (SE). In this area, the most used inputs are time and effort and the most used outputs are source code and functionality. Despite of their known limitations, many of the most used productivity measures are still being used due to the information that they provide for management goals. In order to enable the construction of new productivity measures for SE practitioners, the existence of other inputs apart from time and effort, and other outputs, apart from source code and functionality is analyzed in this paper. Moreover, differences in usage of the inputs and production of the outputs among some SE job positions are analyzed and explained.

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27

Roche, John M. "Software metrics and measurement principles." ACM SIGSOFT Software Engineering Notes 19, no. 1 (January 1994): 77–85. http://dx.doi.org/10.1145/181610.181625.

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28

Rombach, H. D., and B. T. Ulery. "Improving software maintenance through measurement." Proceedings of the IEEE 77, no. 4 (April 1989): 581–95. http://dx.doi.org/10.1109/5.24144.

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29

Bush, Martin E., and Norman E. Fenton. "Software measurement: A conceptual framework." Journal of Systems and Software 12, no. 3 (July 1990): 223–31. http://dx.doi.org/10.1016/0164-1212(90)90043-l.

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30

Baker, Albert L., James M. Bieman, Norman Fenton, David A. Gustafson, Austin Melton, and Robin Whitty. "A philosophy for software measurement." Journal of Systems and Software 12, no. 3 (July 1990): 277–81. http://dx.doi.org/10.1016/0164-1212(90)90050-v.

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31

Robillard, Pierre N., Mathieu Lavallée, Yvan Ton-That, and François Chiocchio. "Taxonomy for software teamwork measurement." Journal of Software: Evolution and Process 26, no. 10 (January 13, 2014): 910–22. http://dx.doi.org/10.1002/smr.1641.

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32

Zaprudskii, V. M., and V. M. Malyshev. "Software for flexible measurement systems." Measurement Techniques 30, no. 11 (November 1987): 1046–48. http://dx.doi.org/10.1007/bf00865050.

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33

Kokol, P. "Application of spreadsheet software in software engineering measurement technology." Information and Software Technology 31, no. 9 (November 1989): 477–85. http://dx.doi.org/10.1016/0950-5849(89)90146-8.

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34

Magdziak, Marek, and Dominika Ziaja. "Software Dedicated to Determining a Strategy of Coordinate Measurements." Materials Science Forum 957 (June 2019): 179–86. http://dx.doi.org/10.4028/www.scientific.net/msf.957.179.

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The paper presents the developed software dedicated to determining a measurement strategy of contact coordinate measurements conducted by using coordinate measuring machines. The created software enables to calculate locations of the scanning lines along free-form surfaces of measured workpieces. The presented program was developed by using the MATLAB software. The created program was tested based on the selected examples of curvilinear surfaces. Measurement points were located in the parts of surfaces characterized by the biggest form deviations resulting from machining processes. The calculated deviations were the results of simulations performed by using selected CAM software. The presented software increases the efficiency of measurement processes.

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35

Wang, Jin Zhu, and Jian Jie Ding. "A Framework for Filtrating Software Measures in Software Measurement Process." Advanced Materials Research 605-607 (December 2012): 2479–82. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2479.

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Software measures filtration is important but often been neglected activity in software measurement. A framework for software measures filtration process that not only satisfied measurement goals but also matched organization capability is been presented. In this framework, software measures that get by GQM been evaluated on the evaluation criteria. The fuzzy mathematic expectation has been proposed to calculate measures evaluation value. The algorithm of verify goal achievable has been described. The framework ensures that measures set are most appropriate.

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36

Tahir, Touseef, Ghulam Rasool, Waqar Mehmood, and Cigdem Gencel. "An Evaluation of Software Measurement Processes in Pakistani Software Industry." IEEE Access 6 (2018): 57868–96. http://dx.doi.org/10.1109/access.2018.2872956.

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37

Carstensen, Jacob, Poul Harremoës, and Rune Strube. "Software sensors based on the grey-box modelling approach." Water Science and Technology 33, no. 1 (January 1, 1996): 117–26. http://dx.doi.org/10.2166/wst.1996.0011.

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In recent years the grey-box modelling approach has been applied to wastewater transportation and treatment. Grey-box models are characterized by the combination of deterministic and stochastic terms to form a model where all the parameters are statistically identifiable from the on-line measurements. With respect to the development of software sensors, the grey-box models possess two important features. Firstly, the on-line measurements can be filtered according to the grey-box model in order to remove noise deriving from the measuring equipment and controlling devices. Secondly, the grey-box models may contain terms which can be estimated on-line by use of the models and measurements. In this paper, it is demonstrated that many storage basins in sewer systems can be used as an on-line flow measurement, provided that the basin is monitored on-line with a level transmitter and that a grey-box model for the specific dynamics is identified. Similarly, an on-line software sensor for detecting the occurrence of backwater phenomena can be developed by comparing the dynamics of a flow measurement with a nearby level measurement. For treatment plants it is found that grey-box models applied to on-line ammonia measurements from the aeration tank of an alternating plant provide information on the incoming ammonia load. It is also shown how measurements of the return sludge concentration from a secondary clarifier can be filtered to minimize the effect of the scraper. Thus, important information can be derived from on-line measurements if the appropriate grey-box model for the specific system is identified.

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38

Kuzmenko, Iuriy, O. M. Samoilenko, and Serhiy Tsiporenko. "MULTIPURPOSE MEASUREMENT MODELS FOR ADJUSTMENT BY THE LEAST-SQUARES METHOD." Measuring Equipment and Metrology 82, no. 2 (2021): 29–37. http://dx.doi.org/10.23939/istcmtm2021.02.029.

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The development of multipurpose measurement models is the precondition for software development for simultaneous adjustment of the large scope and complicated combinations of the measurement results by the least-squares method. Multipurpose measurement models for software can be a helpful tool for processing the final measurement results provided by different measurement methods applying the mentioned software; processing the measurement results of measurement standards comparisons, interlaboratory comparison, and calibration procedures; estimating the additive and multiplicative systematic components of measurement errors and their uncertainty; processing complicated combinations by binding or linking up of the interlaboratory comparison and calibration results in the time; simultaneous processing of the measurement results obtained by various methods e.g. by the method of direct measurements and comparisons; fast-changing the multipurpose measurement models from linear to non-linear type. Processing of the results by software based on the multipurpose measurement model algorithm can help to established a comprehensive measurement traceability network by pooling the single traceability chains.

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39

Meijer, J., and C. J. Heuvelman. "Accuracy of Surface Plate Measurements — General Purpose Software for Flatness Measurement." CIRP Annals 39, no. 1 (1990): 545–48. http://dx.doi.org/10.1016/s0007-8506(07)61115-9.

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40

XU, Qin-gui, Gui-xiong LIU, and Fu-rong GAO. "Software protection model for measurement applications." Journal of Computer Applications 31, no. 4 (June 8, 2011): 970–74. http://dx.doi.org/10.3724/sp.j.1087.2011.00970.

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41

Tariq, Aliza, Mazhar Javed Awan, Jalawi Alshudukhi, Talha Mahboob Alam, Khalid Twarish Alhamazani, and Zelalem Meraf. "Software Measurement by Using Artificial Intelligence." Journal of Nanomaterials 2022 (March 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/7283171.

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Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human-created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human-like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID-19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation.

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42

Kaur, Harmeet, and Gurvinder N. Verma. "Software Complexity Measurement: A Critical Review." International Journal of Engineering and Applied Computer Science 01, no. 01 (November 25, 2016): 12–16. http://dx.doi.org/10.24032/ijeacs/0101/03.

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43

Hao, Rui, Xin Guang Peng, and Lei Xiu. "Research on Software Trusted Dynamic Measurement." Applied Mechanics and Materials 268-270 (December 2012): 1869–72. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1869.

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For the problem of trust chain of Trusted Computing Group (TCG), which only measures static integrity to the system resources, we extend the TCG chain to the software application layer and propose to extract return addresses of functions in call stacks dynamically for obtaining system call short sequences as software behavior and By monitoring softare behavior based on SVM,we realize software trusted dynamic measurement.

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44

Baltzell, Jonathan E. "Software Reviews : Measurement & Scaling Strategist." Social Science Computer Review 8, no. 3 (October 1990): 465–66. http://dx.doi.org/10.1177/089443939000800316.

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45

Fenton, N., P. Krause, and M. Neil. "Software measurement: uncertainty and causal modeling." IEEE Software 19, no. 4 (July 2002): 116–22. http://dx.doi.org/10.1109/ms.2002.1020298.

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46

Ben Hadj Salem Mhamdia, Amel. "Performance measurement practices in software ecosystem." International Journal of Productivity and Performance Management 62, no. 5 (July 22, 2013): 514–33. http://dx.doi.org/10.1108/ijppm-09-2012-0097.

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47

Verbe, S. "Review: Software Quality Assurance & Measurement." Computer Bulletin 38, no. 5 (November 1, 1996): 26. http://dx.doi.org/10.1093/combul/38.5.26.

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48

Kirakowski, Jurek, and Mary Corbett. "SUMI: the Software Usability Measurement Inventory." British Journal of Educational Technology 24, no. 3 (September 1993): 210–12. http://dx.doi.org/10.1111/j.1467-8535.1993.tb00076.x.

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49

Fenton, N. "Software measurement: a necessary scientific basis." IEEE Transactions on Software Engineering 20, no. 3 (March 1994): 199–206. http://dx.doi.org/10.1109/32.268921.

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50

Konrad, Erhard. "Software metrics, measurement theory, and viewpoints." ACM SIGPLAN Notices 26, no. 3 (January 2, 1991): 53–62. http://dx.doi.org/10.1145/122167.122174.

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