DOCSLIB.ORG

University of Macedonia Department of Applied Informatics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

UNIVERSITY OF MACEDONIA DEPARTMENT OF APPLIED INFORMATICS MASTER THESIS A STUDY OF WORK DISTRIBUTION IN OPEN SOURCE SOFTWARE PROJECTS KONSTANTINOS STAMATIADIS ADVISOR: ALEXANDER CHATZIGEORGIOU JANUARY 2012 Konstantinos Stamatiadis [email protected] ii PREFACE In software projects in general and Open Source Software (OSS) projects in particular, the most important aspects are the teams of people that develop them (in OSS we call them the “Community”). As projects grow in size and complexity, so do the teams that develop and maintain them. The emergence of the OSS movement provided software engineering researchers with massive amounts of data from every aspect of the process of developing software, ranging from the social behavior within the teams to various metrics of the code that is being produced. Numerous studies explored how the teams operate [15], [13], evolve [14], [9], the motiva- tion behind the participating developers [10], [18] and the ingredients that affect the quality of the output [1]. The goal of this Thesis is to contribute knowledge in the stud- ies of the social aspect of the OSS movement. We focus on the study of the contribution of the developers in open source projects, by employing the Gini coefficient as a measure of the distribution of effort. Even though the Gini coefficient was used before [5], [17] (albeit in only a few studies and only until recently), this paper, in our knowledge, is the first one to utilize data extracted from a massive source of around 1.200 open source projects, varying in size and duration, thus describing what seems to be the norm, rather than a limited observation. We decided to research how developers contribute to OSS projects because we think (and others too [16]) that it’s one of the factors that indicate how viable is a project (i.e. how active — and in what way — is the community around it) and in an essence influences the deci- sion (for individuals, academics and corporations) on whether or not to invest and get involved in an open source project. The remainder of this Thesis is organized as follows: In the first chapter we make an in- troduction into the empirical studies in software engineering and provide the reasons that are important today. In Chapter 2 we present the FLOSSMetrics project (the source of the data we analyzed), describe what it offers and the challenges it introduces when used. In Chapter 3 we define our specific research target, we describe the decisions we took, how we received the results and what are our findings. Finally in Chapter 4 we conclude our research and propose work for future studies. iii iv ACKNOWLEDGEMENTS I would like to thank my advisor, Alexander Chatzigeorgiou, for suggesting a research area well-suited to both my interests and skills, and for giving me solid advice as I worked on it. His encouragement, enthusiasm and contribution, during numerous, lengthy and productive sessions, always helped me push ahead. Also, I want to thank all my friends, fellow M.Sc. and Ph.D. students and family mem- bers for helping me and believing in me in moments I couldn’t. v vi CONTENTS Preface ................................................................................................................................iii Acknowledgements ............................................................................................................. v Contents ............................................................................................................................ vii List of Figures ..................................................................................................................... ix List of Tables ........................................................................................................................ x List of Source Code ............................................................................................................ xii 1 Introduction..................................................................................................................1 2 FLOSSMetrics .............................................................................................................. 3 2.1 About FLOSSMetrics ........................................................................................... 3 2.2 Data Preparation .................................................................................................. 4 2.3 Schema ................................................................................................................. 5 2.4 Description of Tables .......................................................................................... 11 2.4.1 Description of MLS Tables .............................................................................. 11 2.4.2 Description of SCM Tables .......................................................................... 13 2.4.3 Description of TRK Tables ..............................................................................16 2.5 Working with FLOSSMetrics Data .....................................................................18 2.5.1 Challenges .......................................................................................................18 2.5.2 Working with the Data....................................................................................18 2.5.3 “Bird’s Eye” View of the Data ..........................................................................19 3 Work Distribution ...................................................................................................... 21 3.1 Gini Coefficient ................................................................................................... 21 3.2 Data Retrieval and Preparation ......................................................................... 23 3.3 Gini/Project ........................................................................................................ 24 3.4 Correlations ........................................................................................................ 28 3.4.1 Number of Committers & Gini ...................................................................... 29 3.4.2 Number of Commits & Gini ........................................................................... 30 vii 3.4.3 Project’s Duration & Gini ................................................................................ 31 3.4.4 Aggregated SLOC & Gini............................................................................ 32 3.5 Gini Progress ...................................................................................................... 33 3.6 Survival Analysis ................................................................................................ 36 4 Threats to Validity ..................................................................................................... 39 5 Conclusions and Future Work....................................................................................41 A. Appendix .................................................................................................................... 43 A.1 SQL Queries ....................................................................................................... 43 A.2 MATLAB Code ................................................................................................... 46 A.3 Numerical Data .................................................................................................. 48 Bibliography .......................................................................................................................81 viii LIST OF FIGURES Figure 2-1: Unified schema .................................................................................................. 7 Figure 2-2: MLS schema ...................................................................................................... 8 Figure 2-3: SCM schema ...................................................................................................... 9 Figure 2-4: TRK schema .....................................................................................................10 Figure 3-1: Income disparity since WWII .......................................................................... 22 Figure 3-2: Defining Gini coefficient using a Lorenz curve .............................................. 23 Figure 3-3: Gini coefficient per project ............................................................................. 24 Figure 3-4: Number of projects per Gini coefficient range ............................................... 25 Figure 3-5: Gini coefficient values in a Box Plot ................................................................ 26 Figure 3-6: Correlation coefficient and plotf o committers and Gini coefficient............. 29 Figure 3-7: Correlation coefficient and plot of commits and Gini coefficient ................. 30 Figure 3-8: Correlation coefficient and plot of duration and Gini coefficient .................. 31 Figure 3-9: Correlation coefficient and plot of aggregated SLOC and Gini coefficient ... 32 Figure 3-10: Negative and positive Gini trends (all projects)............................................ 34 Figure 3-11: Negative and positive Gini trends (projects with actual change rate)........... 35 Figure 3-12: Survival Analysis ............................................................................................ 37 ix LIST OF TABLES Table 2-1: Various sizes .......................................................................................................
Recommended publications
  • DICE Framework – Initial Version

    DICE Framework – Initial Version

    Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements DICE Framework – Initial version Deliverable 1.5 Deliverable 1.5. DICE Framework – Initial version Deliverable: D1.5 Title: DICE Framework – Initial version Editor(s): Marc Gil (PRO) Contributor(s): Marc Gil (PRO), Ismael Torres (PRO), Christophe Joubert (PRO) Giuliano Casale (IMP), Darren Whigham (Flexi), Matej Artač (XLAB), Diego Pérez (Zar), Vasilis Papanikolaou (ATC), Francesco Marconi (PMI), Eugenio Gianniti(PMI), Marcello M. Bersani (PMI), Daniel Pop (IEAT), Tatiana Ustinova (IMP), Gabriel Iuhasz (IEAT), Chen Li (IMP), Ioan Gragan (IEAT), Damian Andrew Tamburri (PMI), Jose Merseguer (Zar), Danilo Ardagna (PMI) Reviewers: Darren Whigham (Flexi), Matteo Rossi (PMI) Type (R/P/DEC): - Version: 1.0 Date: 31-January-2017 Status: First Version Dissemination level: Public Download page: http://www.dice-h2020.eu/deliverables/ Copyright: Copyright © 2017, DICE consortium – All rights reserved DICE partners ATC: Athens Technology Centre FLEXI: FlexiOPS IEAT: Institutul e-Austria Timisoara IMP: Imperial College of Science, Technology & Medicine NETF: Netfective Technology SA PMI: Politecnico di Milano PRO: Prodevelop SL XLAB: XLAB razvoj programske opreme in svetovanje d.o.o. ZAR: Universidad De Zaragoza The DICE project (February 2015-January 2018) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 644869 Copyright © 2017, DICE consortium – All rights reserved 2 Deliverable 1.5. DICE Framework – Initial version Executive summary This deliverable documents the DICE Framework, which is composed of a set of tools developed to support the DICE methodology. One of these tools is the DICE IDE, which is the front-end of the DICE methodology and plays a pivotal role in integrating the other tools of the DICE framework.
  • Baxedit Is an Editor for Programs in Basic Usable by Bax58c Compiler

    Baxedit Is an Editor for Programs in Basic Usable by Bax58c Compiler

    BaxEdit is an editor for programs in Basic usable by Bax58C compiler . © 2011 Pierre Houbert (21/11/2020) Page 1 SUMMARY Basic language ................................................................................ 3 The Menus .......................................................................................... 4 New ............................................................................................. 6 Open .................................................................................................. 9 Save .......................................................................................... 12 Print ............................................................................................. 13 Quit ................................................................................................. 15 Cut / Copy / Paste ..................................................................... 16 Find ......................................................................................... 17 Replace .......................................................................................... 18 Font .................................................................................................. 19 Colors ............................................................................................. 20 Language .............................................................................................. 21 About ............................................................................................. 22 File Explorer
  • Data Science with Postgresql

    Data Science with Postgresql

    Data Science with PostgreSQL Data Science with PostgreSQL Balázs Bárány Data Scientist pgconf.eu 2015 Data Science with PostgreSQL Contents Introduction What is Data Science? Process model Tools and methods of Data Scientists Data Science with PostgreSQL Business & data understanding Preprocessing Modeling Evaluation Deployment Summary I Who is a Data Scientist? Data Science with PostgreSQL Introduction What is Data Science? Sexiest job of the 21st century I According to Google, LinkedIn, ... Data Science with PostgreSQL Introduction What is Data Science? Sexiest job of the 21st century I According to Google, LinkedIn, ... I Who is a Data Scientist? Data Science with PostgreSQL Introduction What is Data Science? Data Science Venn Diagram (c) Drew Conway, 2010. CC-BY-NC I Mash up & format for analysis I Analyze & visualize I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Analyze & visualize I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Mash up & format for analysis I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Mash up & format for analysis I Analyze & visualize I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data
  • Software Corner: Rkward: Installation and Use Aaron Olaf Batty Abatty@Sfc.Keio.Ac.Jp SRB General Editor

    Software Corner: Rkward: Installation and Use Aaron Olaf Batty [email protected] SRB General Editor

    Software Corner Software Corner: RKWard: Installation and use Aaron Olaf Batty [email protected] SRB General Editor Many readers of SRB are likely familiar with the free, open-source statistical software package R. R offers a staggeringly wide range of analyses and outputs, besting every other general statistics package on the market, for free. This value, however, comes at a cost that may seem too steep for many users: it is command-line only. One must learn to write R code to take advantage of this software, and although the code itself is relatively memorable and intuitive, it cannot compete with the ease of use offered by the graphical user interfaces (GUIs) of competing packages, such as SPSS/PASW—a software package whose price can run over a thousand US dollars. What is RKWard? RKWard seeks to lower the usability barrier to entry into statistical analysis with R by providing a GUI frontend that hides much of the code from the use (although it is easy to inspect in the con- sole for those who wish to do so). Furthermore, it streamlines the sometimes-maddening process of importing data into R, and can read SPSS and Stata files natively in addition to standard text formats. Finally, its general look-and-feel will be immediately familiar to those who have used SPSS, further easing entry for novices. It has been in development since 2005, and achieved its first stable release in 2011. Although it uses the KDE desktop environment, which is usually found on Linux systems, it is not limited to computers running Linux/Unix.
  • Microsoft Small Basic

    Microsoft Small Basic

    Katolickie Gimnazjum i Liceum Ogólnokształcące im. Jana Pawła II w Łodzi Microsoft Small Basic Translated by Anna Wilk (klasa 2a 2009/2010) Wstęp Small Basic i programowanie Programowanie komputerowe jest zdefiniowane jako proces tworzenia oprogramowania komputerowego używającego języki programowania. Tylko jak mówimy i rozumiemy Anglików albo Hiszpana albo Francuzów, komputery mogą zrozumieć programy napisane w pewnych językach. To tzw. języki programowania. Na początku było niewiele takich języków, były proste i zrozumiałe. Z czasem oprogramowanie i komputery stały się bardziej skomplikowane i wyszukane, więc języki programowania również musiały ewaluować. W efekcie są to trudne do zrozumienia metody programowania, szczególnie dla początkujących. Small Basic przedstawia programowanie w przyjemny, niezwykle łatwy i ciekawy sposób, usuwa bariery, by każdy mógł wkroczyć w świat programowania. Small Basic - Środowisko Zacznijmy od przedstawienia interfejsu Small Basic’a i poruszania się w nim. Po pierwszym uruchomieniu zobaczycie właśnie takie okno: Obrazek 1 To jest Small Basic Środowisko, gdzie napiszemy nasze programy. Opiszemy każdy element zaznaczony numerem. 1. Edytor(1) to miejsce, w którym piszemy program. Otwierając przykładowy program lub poprzednio zapisany, pojawi się on w tym oknie. Możesz otworzyć i korzystać z kilku edytorów na raz. 2. Pasek narzędzi(2) nadaje komendy w edytorze. O różnych zadaniach nauczymy się później. 3. Powierchnia pod edytorem (3) to obszar na wszystkie okna edycji. Nasz pierwszy Program Już znasz podstawy, więc zacznijmy programować. Wpiszmy poniższą linijkę do edytora. TextWindow.WriteLine(„Hello World”) Jeżeli wszystko zostało wpisane poprawnie, to ujżymy coś takiego: Pierwszy Program Teraz, kiedy już napisaliśmy nasz pierwszy program, włączmy go. Możemy to zrobić klikajać na przycisk RUN w pasku narzędzi lub naciskając F5.
  • Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R

    Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R

    JSS Journal of Statistical Software June 2012, Volume 49, Issue 9. http://www.jstatsoft.org/ RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R Stefan R¨odiger Thomas Friedrichsmeier Charit´e-Universit¨atsmedizin Berlin Ruhr-University Bochum Prasenjit Kapat Meik Michalke The Ohio State University Heinrich Heine University Dusseldorf¨ Abstract R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated devel- opment environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself. Keywords: GUI, integrated development environment, plugin, R.
  • Microsoft Small Basic

    Microsoft Small Basic

    Microsoft Small Basic An introduction to Programming Chapter 1 An Introduction Small Basic and Programming Computer Programming is defined as the process of creating computer software using programming languages. Just like we speak and understand English or Spanish or French, computers can understand programs written in certain languages. These are called programming languages. In the beginning there were just a few programming languages and they were really easy to learn and comprehend. But as computers and software became more and more sophisticated, programming languages evolved fast, gathering more complex concepts along the way. As a result most modern programming languages and their concepts are pretty challenging to grasp by a beginner. This fact has started discouraging people from learning or attempting computer programming. Small Basic is a programming language that is designed to make programming extremely easy, approachable and fun for beginners. Small Basic’s intention is to bring down the barrier and serve as a stepping stone to the amazing world of computer programming. The Small Basic Environment Let us start with a quick introduction to the Small Basic Environment. When you first launch SmallBasic, you will see a window that looks like the following figure. Figure 1 - The Small Basic Environment This is the Small Basic Environment, where we’ll write and run our Small Basic programs. This environment has several distinct elements which are identified by numbers. The Editor, identified by [1] is where we will write our Small Basic programs. When you open a sample program or a previously saved program, it will show up on this editor.
  • Molecular Data in R Phylogeny, Evolution & R

    Molecular Data in R Phylogeny, Evolution & R

    Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Molecular data in R Phylogeny, evolution & R Vojtěch Zeisek Department of Botany, Faculty of Science, Charles University, Prague Institute of Botany, Czech Academy of Sciences, Průhonice https://trapa.cz/, [email protected] January 27 to 30, 2020 . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 1 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline I 1 Introduction 2 R Installation Let’s start with R Basic operations in R Packages for our work 3 Data Overview of data and data types Microsatellites AFLP Notes about data DNA sequences, SNP VCF Export . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 2 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline II Tasks 4 Alignment Overview and MAFFT MAFFT, Clustal, MUSCLE and T-Coffee Multiple genes Display and cleaning 5 Basic analysis First look at the data Statistics Genetic distances Hierarchical clustering AMOVA . MSN . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 3 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline III NJ (and UPGMA) tree PCoA Tasks 6 SNP PCA and NJ 7 DAPC Bayesian clustering Discriminant analysis and visualization 8 Structure Running Structure from R ParallelStructure on Windows Post processing 9 Spatial analysis . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 4 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline IV Moran’s I sPCA Monmonier Mantel test Geneland Plotting maps 10 Trees Manipulations MP Seeing trees in the forest Comparisons Notes about plotting the trees .
  • PROCEEDINGS of the ICA CONGRESS (Onl the ICA PROCEEDINGS OF

    PROCEEDINGS of the ICA CONGRESS (Onl the ICA PROCEEDINGS OF

    ine) - ISSN 2415-1599 ISSN ine) - PROCEEDINGS OF THE ICA CONGRESS (onl THE ICA PROCEEDINGS OF Page intentionaly left blank 22nd International Congress on Acoustics ICA 2016 PROCEEDINGS Editors: Federico Miyara Ernesto Accolti Vivian Pasch Nilda Vechiatti X Congreso Iberoamericano de Acústica XIV Congreso Argentino de Acústica XXVI Encontro da Sociedade Brasileira de Acústica 22nd International Congress on Acoustics ICA 2016 : Proceedings / Federico Miyara ... [et al.] ; compilado por Federico Miyara ; Ernesto Accolti. - 1a ed . - Gonnet : Asociación de Acústicos Argentinos, 2016. Libro digital, PDF Archivo Digital: descarga y online ISBN 978-987-24713-6-1 1. Acústica. 2. Acústica Arquitectónica. 3. Electroacústica. I. Miyara, Federico II. Miyara, Federico, comp. III. Accolti, Ernesto, comp. CDD 690.22 ISSN 2415-1599 ISBN 978-987-24713-6-1 © Asociación de Acústicos Argentinos Hecho el depósito que marca la ley 11.723 Disclaimer: The material, information, results, opinions, and/or views in this publication, as well as the claim for authorship and originality, are the sole responsibility of the respective author(s) of each paper, not the International Commission for Acoustics, the Federación Iberoamaricana de Acústica, the Asociación de Acústicos Argentinos or any of their employees, members, authorities, or editors. Except for the cases in which it is expressly stated, the papers have not been subject to peer review. The editors have attempted to accomplish a uniform presentation for all papers and the authors have been given the opportunity
  • BASIC Programming with Unix Introduction

    BASIC Programming with Unix Introduction

    LinuxFocus article number 277 http://linuxfocus.org BASIC programming with Unix by John Perr <johnperr(at)Linuxfocus.org> Abstract: About the author: Developing with Linux or another Unix system in BASIC ? Why not ? Linux user since 1994, he is Various free solutions allows us to use the BASIC language to develop one of the French editors of interpreted or compiled applications. LinuxFocus. _________________ _________________ _________________ Translated to English by: Georges Tarbouriech <gt(at)Linuxfocus.org> Introduction Even if it appeared later than other languages on the computing scene, BASIC quickly became widespread on many non Unix systems as a replacement for the scripting languages natively found on Unix. This is probably the main reason why this language is rarely used by Unix people. Unix had a more powerful scripting language from the first day on. Like other scripting languages, BASIC is mostly an interpreted one and uses a rather simple syntax, without data types, apart from a distinction between strings and numbers. Historically, the name of the language comes from its simplicity and from the fact it allows to easily teach programming to students. Unfortunately, the lack of standardization lead to many different versions mostly incompatible with each other. We can even say there are as many versions as interpreters what makes BASIC hardly portable. Despite these drawbacks and many others that the "true programmers" will remind us, BASIC stays an option to be taken into account to quickly develop small programs. This has been especially true for many years because of the Integrated Development Environment found in Windows versions allowing graphical interface design in a few mouse clicks.
  • Rajasthan Technical University, Kota Detailed Syllabus for B.Tech. (Computer Engineering) Session 2009-10 (Main Examination)

    Rajasthan Technical University, Kota Detailed Syllabus for B.Tech. (Computer Engineering) Session 2009-10 (Main Examination)

    Rajasthan Technical University, Kota Detailed Syllabus for B.Tech. (Computer Engineering) Session 2009-10 (Main Examination) Name of Subject : SOFTWARE ENGINEERING ( 5CS1) Unit Contents System Analysis: Characteristics, Problems in system Development, System Level project Planning, System Development I Life cycle (SDLC), computer system engineering system analysis, modeling the architecture, system specification. Software Project Management: Objectives, Resources and their estimation, LOC and FP estimation, effort estimation, II COCOMO estimation model, risk analysis, software project scheduling. Software Development : Life Cycle (SWDLC), SWDLC models software engineering approaches Requirement Analysis: Requirement analysis tasks, Analysis principles. Software prototyping and specification data III dictionary finite state machine (FSM) models. Structured Analysis: Data and control flow diagrams, control and process specification behavioral modeling, extension for data intensive applications. Software Design: Design fundamentals, Effective modular design: Data architectural and procedural design, design IV documentation. Object Oriented Analysis: Object oriented Analysis Modeling, Data modeling. V Object Oriented Design: OOD concepts and methods class and object definitions, refining operations. Class and object relationships, object modularization. Introduction to Unified Modeling Language Rajasthan Technical University, Kota Detailed Syllabus for B.Tech. (Computer Engineering) Session 2009-10 (Main Examination) Name of Subject : COMPUTER ARCHITECTURE ( 5 CS 2) Unit Contents REGISTER TRANSFER LANGUAGE: Data movement around registers. Data movement from/to memory, arithmetic and I logic micro operations. Concept of bus and timing in register transfer. CPU ORGANISATION: Addressing Modes, Instruction Format. CPU organization with large registers, stacks and handling of II interrupts & subroutines Instruction pipelining ARITHMETIC ALGORITHM: Array multiplier, Booth's algorithm. Addition subtraction for signed unsigned numbers and 2's III complement numbers.
  • The Rebirth of Informix 4GL Legal Notice

    The Rebirth of Informix 4GL Legal Notice

    Lycia The rebirth of Informix 4GL Legal Notice ● The information contained within this presentation is subject to change without notice ● Please submit your feedback to [email protected] ● All rights reserved. No part of this document may be reproduced or transmitted, in any form means, without written consent of Querix (UK) Ltd. ● Lycia, LyciaDesktop, LyciaWeb are trademarks of Querix (UK) Ltd. ● All other products and company names used within this document are trademarks of their respective owners Agenda 1. Evolution of the development software 2. Current challenges 3. The solution you have been waiting for 1. Evolution of the development software 80’s: The success of Informix 4GL ● 2nd most used language at the time on UNIX platforms, after C ● Simple, fast, and powerful way of developing business applications: ○ Easy to learn ○ Native interface with relational databases ○ Use of forms and report writer ○ Support for calling C functions and vice versa ○ Platform independency ○ High performance without requiring huge hardware infrastructures ○ Native 4GL instruction-based debugger 90’s: Domination of the OO languages 00’s: Nothing but WEB Real added value? ● Advanced graphic rendering ● ? Productivity ● It became possible to implement ● ? Application maintenance time functionality that were impossible ● ? Cost savings or too complex to implement ● ? Complexity of developments before ● ? Do code generators help with ● Clouds allow for consistent the real-life applications? infrastructure savings ● ? Savings in IT/IS departments 2017 Querix™