Journal articles on the topic 'OS programmers'

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1

Wilson, Leon A., Maksym Petrenko, and Václav Rajlich. "Using Concept Maps to Assist Program Comprehension and Concept Location: An Empirical Study." Journal of Information & Knowledge Management 11, no. 03 (September 2012): 1250018. http://dx.doi.org/10.1142/s0219649212500189.

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Program comprehension is an integral part of the evolution and maintenance of large software systems. As it is increasingly difficult to comprehend these systems completely, programmers have to rely on a partial and as-needed comprehension. We study partial comprehension and programmer learning with the use of concept maps as a tool for capturing programmer knowledge during concept location, which is one of the tasks of software evolution and maintenance, and it is a prerequisite of a software change. We conduct a user study to measure the performance of programmers using concept maps to assist with locating concepts. The results demonstrate that programmer learning occurs during concept location and that concept maps assisted programmers with capturing programmer learning and successful concept location.
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2

Temkin, Daniel. "Entropy and FatFinger: Challenging the Compulsiveness of Code with Programmatic Anti-Styles." Leonardo 51, no. 4 (August 2018): 405–12. http://dx.doi.org/10.1162/leon_a_01651.

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Coding, the translating of human intent into logical steps, reinforces a compulsive way of thinking, as described in Joseph Weitzenbaum’s “Science and the Compulsive Programmer” (1976). Two projects by the author, Entropy (2010) and FatFinger (2017), challenge this by encouraging gestural approaches to code. In the Entropy programming language, data becomes slightly more approximate each time it is used, drifting from its original values, forcing programmers to be less precise. FatFinger, a Javascript dialect, allows the programmer to misspell code and interprets it as the closest runnable variation, strategically guessing at the programmer’s intent.
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3

NAISH, LEE. "A three-valued semantics for logic programmers." Theory and Practice of Logic Programming 6, no. 5 (August 2, 2006): 509–38. http://dx.doi.org/10.1017/s1471068406002742.

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This paper describes a simpler way for programmers to reason about the correctness of their code. The study of semantics of logic programs has shown strong links between the model theoretic semantics (truth and falsity of atoms in the programmer's interpretation of a program), procedural semantics (for example, SLD resolution) and fixpoint semantics (which is useful for program analysis and alternative execution mechanisms). Most of this work assumes that intended interpretations are two-valued: a ground atom is true (and should succeed according to the procedural semantics) or false (and should not succeed). In reality, intended interpretations are less precise. Programmers consider that some atoms “should not occur” or are “ill-typed” or “inadmissible”. Programmers don't know and don't care whether such atoms succeed. In this paper we propose a three-valued semantics for (essentially) pure Prolog programs with (ground) negation as failure which reflects this. The semantics of Fitting is similar but only associates the third truth value with non-termination. We provide tools to reason about correctness of programs without the need for unnatural precision or undue restrictions on programming style. As well as theoretical results, we provide a programmer-oriented synopsis. This work has come out of work on declarative debugging, where it has been recognised that inadmissible calls are important.
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4

Müller, Matthias M. "Do programmer pairs make different mistakes than solo programmers?" Journal of Systems and Software 80, no. 9 (September 2007): 1460–71. http://dx.doi.org/10.1016/j.jss.2006.10.032.

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5

Neville-Neil, George. "Poisonous Programmers." Queue 6, no. 1 (January 2008): 5–6. http://dx.doi.org/10.1145/1348583.1348585.

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6

McConnell, S. "Problem programmers." IEEE Software 15, no. 2 (1998): 128. http://dx.doi.org/10.1109/52.663801.

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7

Kiesewetter-Kobinger, Swen. "Programmers' Capital." Computer 43, no. 2 (February 2010): 106–7. http://dx.doi.org/10.1109/mc.2010.47.

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8

Prihozhy, A. A., and A. M. Zhdanouski. "METHOD OF QUALIFICATION ESTIMATION AND OPTIMIZATION OF PROFESSIONAL TEAMS OF PROGRAMMERS." «System analysis and applied information science», no. 2 (August 7, 2018): 4–11. http://dx.doi.org/10.21122/2309-4923-2018-2-4-11.

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The problem of building and optimizing the teams of programmers taking into account the qualification and the level of skills in programming technologies and tools is considered. Known technological environments for the management of teamwork, such as Agile, formulate only general requirements and principles for building the teams and the distribution of work between them. A method for formalizing and evaluating the qualification of individual programmers and entire groups of programmers has been proposed. The input data are attributes of the most popular technologies and programming tools, including technology rating, as well as the results of a survey of programmers on their level of skills. The qualification of a group of programmers is evaluated taking into account the requirements for a particular project, which integrates three components: the average qualification of programmers included in the group; the qualification of the group with respect to the best representatives for each of the technologies; threshold values of the levels of programmer qualification and group qualification for each of the technologies, as well as threshold values of the integrated qualification, reflecting the specifics of the given project. The contribution of each component is determined by appropriate weights. The proposed method is a basis for a genetic algorithm that performs the search for the number, size and staff of groups of programmers which yield a maximum of total qualification. Software has been developed and computer experiments have been carried out on a set of programmers who graduated from Belarusian universities. The obtained results demonstrate the real ability of the system to find the number, size and staff of groups of programmers, which increase the overall qualification of groups by 30% while minimizing the number of unemployed developers. The results prove the practical importance of the method and software in the field of technologies and tools for the management of professional teams of programmers.
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9

Xue Wang, Xue Wang, Yong Wang Xue Wang, Fei Yang Yong Wang, Wenge Le Fei Yang, and Shouhang Wang Wenge Le. "Measuring Programming Ability for Novice Programmers." 網際網路技術學刊 23, no. 3 (May 2022): 573–81. http://dx.doi.org/10.53106/160792642022052303015.

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<p>Coding is a key activity in the software development process and a programmer&rsquo;s programming ability determines the software quality. Different from professional programmers, novice programmers usually refers to programmers who have learned a programming language for about three years. At this stage, measuring their programming ability is of great significance to improve their programming abilities. In previous work, researchers have proposed a variety of ways to measure programming ability for professional programmers. We set out to find out the best way to measure novice programming ability. We first exacted a questionnaire from published comprehension experiments for measuring programming ability. Then, we performed control experiments to compare the answers to the questionnaire with their performance. We found that module number and the number of programming-related websites visited seem to be a reliable way to measure programming ability for novice programmers. Furthermore, we perform exploratory factor analysis to generate a model to verify the effectiveness of our findings.</p> <p>&nbsp;</p>
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10

Liu, Dapeng, Shaochun Xu, and Huafu Liu. "An Empirical Study on Novice Programmer’s Behaviors with Analysis of Keystrokes." International Journal of Software Innovation 1, no. 1 (January 2013): 68–87. http://dx.doi.org/10.4018/ijsi.2013010106.

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This paper presents a series of two experiments in which programming behaviors were observed and analyzed when they were programming with pressure and without pressure. There were eleven and twenty-four subjects respectively. In both experiments, the authors used a software tool to record the keystroke frequency, designed criteria to evaluation program quality, and conducted a survey after the experiment. The experiment results show that there is no direct relation between the numbers of keystrokes and programmer’s performance when programmers are working without pressure or with pressure. The first experiment results demonstrate while novice programmers are diverse in terms of programming styles, ones with more experiences tend to control code execution in finer granularity. Source code format can be an indicator of programming performance. The second experiment results demonstrate that programmers with higher performance likely have higher keystroke productivity. Programmers are also more productive under pressure in terms of keystrokes.
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11

Башкирова, О. А., and Е. А. Башкирова. "Значимость изучения профессионально-ориентированного английского языка будущими программистами в вузе." ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 70, no. 4 (2021): 25–28. http://dx.doi.org/10.18411/lj-02-2021-120.

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The article defines the arguments that help to understand the importance of mastering professional English by future programmers. The results of the survey are presented to determine the number of students who are aware of the importance of learning English in the formation of a competitive programmer. Attention is drawn to the formation of motivation to professional English learning. It is concluded that future programmers need to learn professional English.
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12

Nguyen, Tam The, and Tung Thanh Nguyen. "PERSONA: A personalized model for code recommendation." PLOS ONE 16, no. 11 (November 16, 2021): e0259834. http://dx.doi.org/10.1371/journal.pone.0259834.

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Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract the common code patterns, and utilize the patterns for recommendations. However, programmers are different in multiple aspects including coding preferences, styles, levels of experience, and knowledge about libraries and frameworks. These differences lead to various usages of code elements. When the code of multiple programmers is combined and mined, such differences are disappeared, which could limit the accuracy of the code recommendation tool for a specific programmer. In the paper, we develop a code recommendation technique that focuses on the personal coding patterns of programmers. We propose Persona, a personalized code recommendation model. It learns personalized code patterns for each programmer based on their coding history, while also combines with project-specific and common code patterns. Persona supports recommending code elements including variable names, class names, methods, and parameters. The empirical evaluation suggests that our recommendation tool based on Persona is highly effective. It recommends the next identifier with top-1 accuracy of 60-65% and outperforms the baseline approaches.
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13

Shrestha, Nischal, Colton Botta, Titus Barik, and Chris Parnin. "Here we go again." Communications of the ACM 65, no. 3 (March 2022): 91–99. http://dx.doi.org/10.1145/3511062.

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Once a programmer knows one language, they can leverage concepts and knowledge already learned, and easily pick up another programming language. But is that always the case? To understand if programmers have difficulty learning additional programming languages, we conducted an empirical study of Stack Overflow questions across 18 different programming languages. We hypothesized that previous knowledge could potentially interfere with learning a new programming language. From our inspection of 450 Stack Overflow questions, we found 276 instances of interference that occurred due to faulty assumptions originating from knowledge about a different language. To understand why these difficulties occurred, we conducted semistructured interviews with 16 professional programmers. The interviews revealed that programmers make failed attempts to relate a new programming language with what they already know. Our findings inform design implications for technical authors, toolsmiths, and language designers, such as designing documentation and automated tools that reduce interference, anticipating uncommon language transitions during language design, and welcoming programmers not just into a language, but its entire ecosystem.
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14

Prihozhy, A. A., and A. M. Zhdanouski. "Genetic algorithm of optimizing the qualification of programmer teams." «System analysis and applied information science», no. 4 (January 28, 2021): 31–38. http://dx.doi.org/10.21122/2309-4923-2020-4-31-38.

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The partitioning a set of professional programmers into a set of teams when a programming project specifies requirements to the competency in various programming technologies and tools is a hard combinatorial problem. The paper proposes a genetic algorithm, which is capable of finding competitive and high-quality partitioning solutions in acceptable runtime. The algorithm introduces chromosomes in such a way as to assign each programmer to a team, define the team staff and easily reconstruct the teams during optimization process. A fitness function characterizes each chromosome with respect to the quality of the programmers partitioning. It accounts for the average qualification of teams and the qualification of team best representatives on each of the technologies. The function recognizes the teams that meet all constraints on the project and are workable from this point of view. It is also capable of recognizing the teams that do not meet the constraints and are unworkable. The algorithm defines the genetic operations of selection, crossing and mutation in such a way as to move programmers from unworkable to workable teams, to increase the number of workable teams, to ex-change programmers among workable teams, to increase the competency of every workable team, and thus to maximize the teams overall qualification. Experimental results obtained on a set of programmers graduated from Belarus universities show the capability of the genetic algorithm to find good partitioning solutions, maximize the teams’ competency and minimize the number of unemployed programmers.
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15

Beal, Don. "ICCA Programmers’ Contacts." ICGA Journal 17, no. 4 (December 1, 1994): 219. http://dx.doi.org/10.3233/icg-1994-17406.

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16

Manns, Mary Lynn, and David A. Carlson. "Retraining procedural programmers." ACM SIGPLAN OOPS Messenger 4, no. 2 (April 1993): 131–33. http://dx.doi.org/10.1145/157710.157733.

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17

Florentin, J. "Programmers or designers?" Computer Bulletin 40, no. 4 (July 1, 1998): 14. http://dx.doi.org/10.1093/combul/40.4.14.

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18

Poulin, J. S. "Experienced, programmers only." IEEE Software 16, no. 3 (May 1999): 105–6. http://dx.doi.org/10.1109/ms.1999.765793.

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19

Aron, Jacob. "Ditch the programmers." New Scientist 224, no. 2993 (November 2014): 21. http://dx.doi.org/10.1016/s0262-4079(14)62086-8.

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20

Cottam, Ian. "ABC programmers handbook." Science of Computer Programming 16, no. 3 (October 1991): 279–80. http://dx.doi.org/10.1016/0167-6423(91)90012-m.

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21

Ross, StevenJ. "Controlling systems programmers." Computer Fraud & Security Bulletin 12, no. 2 (December 1989): 7–10. http://dx.doi.org/10.1016/0142-0496(89)90027-1.

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22

Clement, Andrew, and Marilyn Mantei. "PUMS and programmers." ACM SIGCHI Bulletin 21, no. 4 (April 1990): 70. http://dx.doi.org/10.1145/379106.379124.

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23

Larner, A. "Cfor Corporate Programmers." Computer Journal 37, no. 6 (January 1, 1994): 554. http://dx.doi.org/10.1093/comjnl/37.6.554.

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24

Helm, Richard, and Kim Marriott. "Prolog for programmers." Journal of Logic Programming 4, no. 1 (March 1987): 83–84. http://dx.doi.org/10.1016/0743-1066(87)90023-9.

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25

Tebra, Hans. "Prolog for programmers." Future Generation Computer Systems 2, no. 4 (December 1986): 261–62. http://dx.doi.org/10.1016/0167-739x(86)90025-7.

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26

van de Riet, R. P. "Prolog for programmers." Future Generation Computer Systems 3, no. 3 (September 1987): 221. http://dx.doi.org/10.1016/0167-739x(87)90018-5.

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27

Poon, Pak-Lok, Fei-Ching Kuo, Huai Liu, and Tsong Yueh Chen. "How can non-technical end users effectively test their spreadsheets?" Information Technology & People 27, no. 4 (October 28, 2014): 440–62. http://dx.doi.org/10.1108/itp-01-2013-0004.

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Purpose – An alarming number of spreadsheet faults have been reported in the literature, indicating that effective and easy-to-apply spreadsheet testing techniques are not available for “non-technical,” end-user programmers. The purpose of this paper is to alleviate the problem by introducing a metamorphic testing (MT) technique for spreadsheets. Design/methodology/approach – The paper discussed four common challenges encountered by end-user programmers when testing a spreadsheet. The MT technique was then discussed and how it could be used to solve the common challenges was explained. An experiment involving several “real-world” spreadsheets was performed to determine the viability and effectiveness of MT. Findings – The experiment confirmed that MT is highly effective in spreadsheet fault detection, and yet MT is a general technique that can be easily used by end-user programmers to test a large variety of spreadsheet applications. Originality/value – The paper provides a detailed discussion of some common challenges of spreadsheet testing encountered by end-user programmers. To the best of the authors knowledge, the paper is the first that includes an empirical study of how effective MT is in spreadsheet fault detection from an end-user programmer's perspective.
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28

De Raadt, Michael. "Searching for Tomorrow's Programmers." Issues in Informing Science and Information Technology 1 (2004): 0597–603. http://dx.doi.org/10.28945/763.

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29

Koster, Alexis. "PROLOG for COBOL Programmers." Journal of Research on Computing in Education 21, no. 3 (March 1989): 326–38. http://dx.doi.org/10.1080/08886504.1989.10781883.

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30

Ketler, Karen, Robert D. Smith, and Jay Weinroth. "RECRUITING FOURTH-GENERATION PROGRAMMERS." Information Systems Management 9, no. 4 (January 1992): 64–67. http://dx.doi.org/10.1080/10580539208906903.

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31

Gray, Paul. "PROGRAMMERS, EXECUTIVES, AND KNOWLEDGE." Information Systems Management 10, no. 1 (January 1993): 80–82. http://dx.doi.org/10.1080/10580539308906920.

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32

Rising, Linda. "What Programmers Should Know." IEEE Software 27, no. 4 (July 2010): 15–17. http://dx.doi.org/10.1109/ms.2010.110.

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33

Jenkins, D. G. "Pascal for Fortran Programmers." Software & Microsystems 4, no. 5-6 (1985): 126. http://dx.doi.org/10.1049/sm.1985.0030.

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34

Freeman, John, Jaakko Järvi, Wonseok Kim, Mat Marcus, and Sean Parent. "Helping programmers help users." ACM SIGPLAN Notices 47, no. 3 (April 18, 2012): 177–84. http://dx.doi.org/10.1145/2189751.2047892.

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35

Woodfield, S. N., D. W. Embley, and D. T. Scott. "Can Programmers Reuse Software?" IEEE Software 4, no. 4 (July 1987): 52–59. http://dx.doi.org/10.1109/ms.1987.231064.

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36

Jeffries, R. "Designing interfaces for programmers." IEEE Software 14, no. 6 (1997): 89–91. http://dx.doi.org/10.1109/52.636683.

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37

Arnold, Ken. "Programmers Are People, too." Queue 3, no. 5 (June 2005): 54–59. http://dx.doi.org/10.1145/1071713.1071731.

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38

Partridge, John R., and Brian H. Kleiner. "Managing Computer Programmers Effectively." Industrial Management & Data Systems 92, no. 8 (August 1992): 11–13. http://dx.doi.org/10.1108/02635579210019811.

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39

Cusumano, Michael A. "More lawyers than programmers?" Communications of the ACM 47, no. 7 (July 2004): 29–31. http://dx.doi.org/10.1145/1005817.1005842.

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40

Boundy, David. "A taxonomy of programmers." ACM SIGSOFT Software Engineering Notes 16, no. 4 (October 1991): 23–30. http://dx.doi.org/10.1145/122552.122553.

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41

Pemberton, Steven. "Programmers are humans too." ACM SIGCHI Bulletin 28, no. 1 (January 1996): 96. http://dx.doi.org/10.1145/249170.249191.

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42

Pitt, Robert. "Programmers' workbenches — practical experience." Data Processing 27, no. 9 (November 1985): 16–21. http://dx.doi.org/10.1016/0011-684x(85)90165-0.

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43

DeTar, DelosF. "Pascal for FORTRAN programmers." Computers & Chemistry 9, no. 4 (January 1985): 325. http://dx.doi.org/10.1016/0097-8485(85)85010-5.

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44

OLIVEIRA, BRUNO C. D. S., and JEREMY GIBBONS. "Scala for generic programmers." Journal of Functional Programming 20, no. 3-4 (July 2010): 303–52. http://dx.doi.org/10.1017/s0956796810000171.

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AbstractDatatype-generic programming (DGP) involves parametrization of programs by the shape of data, in the form of type constructors such as ‘list of’. Most approaches to DGP are developed in pure functional programming languages such as Haskell. We argue that the functional object-oriented language Scala is in many ways a better choice. Not only does Scala provide equivalents of all the necessary functional programming features (such as parametric polymorphism, higher-order functions, higher-kinded type operations, and type- and constructor-classes), but it also provides the most useful features of object-oriented languages (such as subtyping, overriding, traditional single inheritance, and multiple inheritance in the form of traits). Common Haskell techniques for DGP can be conveniently replicated in Scala, whereas the extra expressivity provides some important additional benefits in terms of extensibility and reuse. We illustrate this by comparing two simple approaches in Haskell, pointing out their limitations and showing how equivalent approaches in Scala address some of these limitations. Finally, we present three case studies on how to implement in Scala real DGP approaches from the literature: Hinze's ‘Generics for the Masses’, Lämmel and Peyton Jones's ‘Scrap your Boilerplate with Class’, and Gibbons's ‘Origami Programming’.
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45

Shute, M. J. "Pascal for fortran programmers." Microelectronics Journal 16, no. 2 (March 1985): 57. http://dx.doi.org/10.1016/s0026-2692(85)80218-4.

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46

Reiter, Raymond. "Must logicists become programmers?" Computational Intelligence 3, no. 1 (February 1987): 206–7. http://dx.doi.org/10.1111/j.1467-8640.1987.tb00203.x.

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47

Bateson, Bill. "PASCAL for FORTRAN programmers." Microprocessors and Microsystems 9, no. 3 (April 1985): 141. http://dx.doi.org/10.1016/0141-9331(85)90366-7.

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48

Francis, Terri Simone. "Introduction: Film Programmers Speak." JCMS: Journal of Cinema and Media Studies 62, no. 1 (September 2022): 158–61. http://dx.doi.org/10.1353/cj.2022.0067.

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49

Lubin, Justin, and Sarah E. Chasins. "How statically-typed functional programmers write code." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 2021): 1–30. http://dx.doi.org/10.1145/3485532.

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How working statically-typed functional programmers write code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers employ. We conducted a grounded theory analysis of 30 programming sessions of practicing statically-typed functional programmers, 15 of which also included a semi-structured interview. The theory we developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory and reasoning strategies, and expressions of intent. We conducted a set of quantitative lab experiments to validate our findings, including that statically-typed functional programmers often iterate between editing types and expressions, that they often run their compiler on code even when they know it will not successfully compile, and that they make textual program edits that reliably signal future edits that they intend to make. Lastly, we outline the implications of our findings for language and tool design. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer populations.
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Getov, Vladimir, Paul Gray, Sava Mintchev, and Vaidy Sunderam. "Multi-Language Programming Environments for High Performance Java Computing." Scientific Programming 7, no. 2 (1999): 139–46. http://dx.doi.org/10.1155/1999/975837.

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Recent developments in processor capabilities, software tools, programming languages and programming paradigms have brought about new approaches to high performance computing. A steadfast component of this dynamic evolution has been the scientific community’s reliance on established scientific packages. As a consequence, programmers of high‐performance applications are reluctant to embrace evolving languages such as Java. This paper describes the Java‐to‐C Interface (JCI) tool which provides application programmers wishing to use Java with immediate accessibility to existing scientific packages. The JCI tool also facilitates rapid development and reuse of existing code. These benefits are provided at minimal cost to the programmer. While beneficial to the programmer, the additional advantages of mixed‐language programming in terms of application performance and portability are addressed in detail within the context of this paper. In addition, we discuss how the JCI tool is complementing other ongoing projects such as IBM’s High‐Performance Compiler for Java (HPCJ) and IceT’s metacomputing environment.
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