05182nam 2200601 a 450 991046311880332120200520144314.03-95489-540-4(CKB)2670000000422357(EBL)1324010(OCoLC)854977184(SSID)ssj0001154054(PQKBManifestationID)11676750(PQKBTitleCode)TC0001154054(PQKBWorkID)11161009(PQKB)10899449(MiAaPQ)EBC1324010(Au-PeEL)EBL1324010(CaPaEBR)ebr10735066(EXLCZ)99267000000042235720130729d2013 uy 0engur|n|---|||||txtccrCan static type systems speed up programming?[electronic resource] an experimental evaluation of static and dynamic type systems /Sebastian KleinschmagerHamburg Anchor Academic Pub.20131 online resource (114 p.)"Disseminate knowledge"--Cover.3-95489-040-2 Includes bibliographical references.Can static type systems speed up programming? An experimental evaluation of static and dynamic type systems; Abstract; Zusammenfassung (German Abstract); Table of Contents; Directory of Figures; Directory of Tables; Directory of Listings; 1. Introduction; 2. Motivation & Background; 2.1 Motivation; 2.2 Maintenance and Debugging; 2.2.1 Maintenance in a Nutshell; 2.2.2 Debugging in a Nutshell; 2.3 Documentation and APIs; 2.3.1 Documentation of Software Systems; 2.3.2 APIs and Application of their Design Principles in General Programming; 2.4 Type Systems2.5 Empirical Research in Software Engineering2.5.1 On Empirical Research; 2.5.2 Controlled Experiments; 2.5.3 Current State of Empirical Research in Software Engineering; 3. Related Work; 3.1 Gannon (1977); 3.2 Prechelt and Tichy (1998); 3.3 Daly, Sazawal and Foster (2009); 3.4 Hanenberg (2010); 3.5 Steinberg, Mayer, Stuchlik and Hanenberg - A running Experiment series; 3.5.1 Steinberg (2011); 3.5.2 Mayer (2011); 3.5.3 Stuchlik and Hanenberg (2011); 4. The Experiment; 4.1 The Research Question; 4.2 Experiment Overview; 4.2.1 Initial Considerations4.2.2 Further Considerations: Studies on Using Students as Subjects4.2.3 Design of the Experiment; 4.3 Questionnaire; 4.4 Hard- and Software Environment; 4.4.1 Environment; 4.4.2 Programming Languages; 4.4.2.1 Java; 4.4.2.2 Groovy; 4.5 Workspace Applications and Tasks; 4.5.1 The Java Application - A Labyrinth Game; 4.5.2 The Groovy Application - A simple Mail Viewer; 4.5.3 Important Changes made to both Parts; 4.5.4 The Tasks; 4.5.4.1 The Task Types; 4.5.4.2 Tasks 1 and 10 - 2 Types to identify; 4.5.4.3 Tasks 2 and 11 - 4 Types to identify; 4.5.4.4 Tasks 4 and 13 - Semantic Error4.5.4.5 Tasks 5 and 14 - Semantic Error4.5.4.6 Tasks 6 and 15 - 8 Types to identify; 4.5.4.7 Tasks 7 and 16 - Stack size 2 and branch size 3; 4.5.4.8 Tasks 8 and 17 - 12 types to identify; 4.5.4.9 Tasks 9 and 18 - Stack size 2 and branch size 5; 4.5.4.10 Summary of Variables and Mapping of Tasks to Hypotheses; 4.6 Experiment Implementation; 5. Threats to Validity; 5.1 Internal Validity; 5.2 External Validity; 6. Analysis and Results; 6.1 General Descriptive Statistics; 6.2 Statistical Tests and Analysis; 6.2.1 Within-Subject Analysis on the complete data6.2.2 Analysis for residual effects between the two ParticipantGroups6.2.3 Within-Subject Analysis on the two Participant Groups; 6.2.3.1 Participants that started with Groovy; 6.2.3.2 Participants that started with Java; 6.2.4 Exploratory Analysis of the Results based on Participants' Performance; 6.2.4.1 Participants that started with Groovy; 6.2.4.2 Participants that started with Java; 6.2.5 Hypotheses and Task based Analysis; 6.2.5.1 Tasks 1, 2, 3, 6 and 8; 6.2.5.2 Hypothesis 2-1 and Tasks 7 and 9; 6.2.5.3 Hypothesis 2-2 and Tasks 4 and 5; 7. Summary and Discussion; 7.1 Final Remarks7.2 Result SummaryHauptbeschreibung Programming languages that use the object-oriented approach have been around for quite a while now. Most of them use either a static or a dynamic type system. However, both types are very common in the industry. But, in spite of their common use in science and practice, only very few scientific studies have tried to evaluate the two type systems'' usefulness in certain scenarios. There are arguments for both systems. For example, static type systems are said to aid the programmer in the prevention of type errors, and further, they provide documentation help for, there is an eComputer programmingApplication softwareElectronic books.Computer programming.Application software.006.22Kleinschmager Sebastian865795MiAaPQMiAaPQMiAaPQBOOK9910463118803321Can static type systems speed up programming1932128UNINA04885nam 22008295 450 991014359620332120251116234152.03-540-45720-810.1007/3-540-45720-8(CKB)1000000000211494(SSID)ssj0000322263(PQKBManifestationID)11213939(PQKBTitleCode)TC0000322263(PQKBWorkID)10282312(PQKB)11300641(DE-He213)978-3-540-45720-6(MiAaPQ)EBC3072060(PPN)155225103(EXLCZ)99100000000021149420121227d2001 u| 0engurnn#008mamaatxtccrConnectionist Models of Neurons, Learning Processes, and Artificial Intelligence 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13-15, 2001, Proceedings, Part I /edited by Jose Mira, Alberto Prieto1st ed. 2001.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2001.1 online resource (XXVIII, 840 p.)Lecture Notes in Computer Science,0302-9743 ;2084Bibliographic Level Mode of Issuance: Monograph3-540-42235-8 Includes bibliographical references and indexes.Foundations of Connectionism and Biophysical Models of Neurons -- Structural and Functional Models of Neurons -- Learning and Other Plasticity Phenomena, and Complex Systems Dynamics -- Artificial Intelligence and Cognitive Processes.Underlying most of the IWANN calls for papers is the aim to reassume some of the motivations of the groundwork stages of biocybernetics and the later bionics formulations and to try to reconsider the present value of two basic questions. The?rstoneis:“Whatdoesneurosciencebringintocomputation(thenew bionics)?” That is to say, how can we seek inspiration in biology? Titles such as “computational intelligence”, “arti?cial neural nets”, “genetic algorithms”, “evolutionary hardware”, “evolutive architectures”, “embryonics”, “sensory n- romorphic systems”, and “emotional robotics” are representatives of the present interest in “biological electronics” (bionics). Thesecondquestionis:“Whatcanreturncomputationtoneuroscience(the new neurocybernetics)?” That is to say, how can mathematics, electronics, c- puter science, and arti?cial intelligence help the neurobiologists to improve their experimental data modeling and to move a step forward towards the understa- ing of the nervous system? Relevant here are the general philosophy of the IWANN conferences, the sustained interdisciplinary approach, and the global strategy, again and again to bring together physiologists and computer experts to consider the common and pertinent questions and the shared methods to answer these questions.Lecture Notes in Computer Science,0302-9743 ;2084Artificial intelligenceComputersAlgorithmsNeurosciencesNeurologyBioinformaticsComputational biologyArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Neuroscienceshttps://scigraph.springernature.com/ontologies/product-market-codes/B18006Neurologyhttps://scigraph.springernature.com/ontologies/product-market-codes/H36001Computer Appl. in Life Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/L17004Artificial intelligence.Computers.Algorithms.Neurosciences.Neurology.Bioinformatics.Computational biology.Artificial Intelligence.Computation by Abstract Devices.Algorithm Analysis and Problem Complexity.Neurosciences.Neurology.Computer Appl. in Life Sciences.573.8Mira Joseedthttp://id.loc.gov/vocabulary/relators/edtPrieto Albertoedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910143596203321Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence1904953UNINA