This document discusses software project management techniques that can be applied to AI-oriented software projects. It begins with an introduction to AI projects and software project management. It then covers topics like software design methodologies, programming languages, libraries, testing, and examples of AI software implemented using different technologies. The overall message is that AI projects require specialized software and can benefit from established project management practices to help deal with their unpredictable nature.

1. The software management and engineering in the AI-oriented projects Dr W Pietruszkiewicz SDART Ltd

3. Introduction

4. Artificial Intelligence

5. Project success

8. AI & software in projects   Proper management Struggle

11. Software project management

13. Software development schema

15. CRISP-DM

16. SEMMA

17. DMAIC&DMADV

19. Common risk sources

22. Software quality

27. M&LC– staged delivery Requirements Design Implementation Verification Maintenance

29. M&LC – staged delivery Conception Analysis Desing Stage 1 Stage 2 Stage n

30. M&LC – design to schedule Conception Analysis Desing Stage 1 Stage 2 Stage n Software release Time? Costs?

34. M&LC - SCRUM

39. Collaboration Homogeneous research team Heterogeneous software team

40. Project controlling

41. Software design

45. Frameworks - 4+1

48. UML Diagrams

57. Supporting tools & technologies

60. Automated testing - make An example of makefile taken from QT library. PNGMAK=scripts/makefile.linux all: cd zlib; ./configure; make cd libpng; make -f $(PNGMAK) clean: -cd zlib; make clean -cd libpng; make -f $(PNGMAK) clean

61. Automated testing - Ant <project name=&quot;MyProject&quot; default=&quot;dist&quot; basedir=&quot;.&quot;> <description>simple example build file</description> <!-- set global properties for this build --> <property name=&quot;src&quot; location=&quot;src&quot;/> <property name=&quot;build&quot; location=&quot;build&quot;/> <property name=&quot;dist&quot; location=&quot;dist&quot;/> <target name=&quot;init&quot;> <!-- Create the time stamp --> <tstamp/> <!-- Create the build directory structure used by compile --> <mkdir dir=&quot;${build}&quot;/> </target> <target name=&quot;compile&quot; depends=&quot;init ” description=&quot;compile the source &quot; > <!-- Compile the java code from ${src} into ${build} --> <javac srcdir=&quot;${src}&quot; destdir=&quot;${build}&quot;/> </target> <target name=&quot;dist&quot; depends=&quot;compile ” description=&quot;generate the distribution&quot; > <!-- Create the distribution directory --> <mkdir dir=&quot;${dist}/lib&quot;/> <!-- Put everything in ${build} into the MyProject-${DSTAMP}.jar file --> <jar jarfile=&quot;${dist}/lib/MyProject-${DSTAMP}.jar&quot; basedir=&quot;${build}&quot;/> </target> <target name=&quot;clean ” description=&quot;clean up&quot; > <!-- Delete the ${build} and ${dist} directory trees --> <delete dir=&quot;${build}&quot;/> <delete dir=&quot;${dist}&quot;/> </target> </project>

62. Automated testing - Maven <project xmlns=&quot;http://maven.apache.org/POM/4.0.0&quot; mlns:xsi=&quot;http://www.w3.org/2001/XMLSchema-instance&quot; xsi:schemaLocation=&quot;http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd&quot;> <modelVersion>4.0.0</modelVersion> <groupId>com.mycompany.app</groupId> <artifactId>my-app</artifactId> <packaging>jar</packaging> <version>1.0-SNAPSHOT</version> <name>Maven Quick Start Archetype</name> <url>http://maven.apache.org</url> <dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>3.8.1</version> <scope>test</scope> </dependency> </dependencies> </project>

63. Automated testing - unit The most popular unit testing tool is xUnit available in many versions for all popular languages Test A Test ABCD vs Test B Test C Test D Data Data Data Data Data

67. Bug tracking – bug categories

69. Bug tracking

70. AI implementation

72. Levels of programming languages

85. Languages popularity http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html

91. Computer languages Scientific perspective Programming perspective

98. More important part of AI software Knowledge Models Rules Graphs

99. Hybrid software development Research Development Idea Product Replanning & redesign Technology X Technology X Technology Y ?? ??? ??

108. Thank you. Any questions? Get in touch: www.sdart.co.uk