Artificial Intelligence for Software Quality Improvement

Authors: Martín Agüero, Franco Madou, Gabriela Esperón, Daniela López De Luise

Abstract:

This paper presents a software quality support tool, a Java source code evaluator and a code profiler based on computational intelligence techniques. It is Java prototype software developed by AI Group [1] from the Research Laboratories at Universidad de Palermo: an Intelligent Java Analyzer (in Spanish: Analizador Java Inteligente, AJI). It represents a new approach to evaluate and identify inaccurate source code usage and transitively, the software product itself. The aim of this project is to provide the software development industry with a new tool to increase software quality by extending the value of source code metrics through computational intelligence.

Keywords: Software metrics, artificial intelligence, neuralnetworks, clustering algorithms, expert systems

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076392

References:

[1] AI Group: http://www.palermo.edu/ingenieria/it_lab.html
[2] Roe and Lytle, pp. 99, 1935.
[3] Moore, pp. 652, 1958.
[4] James D. Arthur, "Managing Software Quality: A Measurement Framework for Assessments and Prediction", Springer, 2002.
[5] ISO/IEC 9126: http://www.cse.dcu.ie/essiscope/sm2/9126ref.html
[6] Roger S. Pressman, "Ingeniería del Software: Un Enfoque Práctico", Mc Graw Hill, 1998.
[7] Stephen H. Kan, "Metrics and Models in Software Quality Engineering", Addison-Wesley Professional, 2002.
[8] Capers Jones, "Applied software measurement: assuring productivity and quality", Mc Graw Hill, 1996.
[9] National Institute of Standards and Technology, "The Economic Impacts of Inadequate Infrastructure for Software Testing", RTI, 2002.
[10] W.A. Woods, "Transition Network Grammars for Natural Language Analysis", pp. 591-606, Communications of the ACM, 1970.
[11] Bruce Eckel, "Thinking in Patterns", 2003.
[12] James Gosling, Bill Joy, Guy Steele, Gilad Bracha "The Java Language Specification 3rd Edition ", Pretience Hall, 2005.
[13] Daniela L├│pez De Luise, Mart├¡n Ag├╝ero, "Aplicaci├│n de Métricas Categ├│ricas en Sistemas con L├│gica Difusa", Revista IEEE América Latina, 2007.
[14] Patrick H. Winston, "Inteligencia Artificial, tercera edici├│n", Addison Wesley Iberoamericana, 1992.
[15] Ian H. Witten, Eibe Frank "Data Mining: Practical Machine Learning Tools and Techniques", pp. 265, Morgan Kaufmann, 2005.
[16] Charles Forgy, "Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem", Artificial Intelligence, 19, pp 17-37, 1982.
[17] Madou F, Ag├╝ero M., Esper├│n G., L├│pez De Luise D., "Sistemas Expertos en Evaluaci├│n de Calidad Java", CONESCAPAN, 2009.
[18] Ag├╝ero M., Esper├│n G., Madou F, L├│pez De Luise D., "Intelligent Java Analyzer", IEEE CERMA, 2008.
[19] CLIPS http://clipsrules.sourceforge.net/
[20] JESS http://www.jessrules.com
[21] DROOLS http://www.jboss.org/drools/drools-expert.html
[22] Madou F, Ag├╝ero M., Esper├│n G., L├│pez De Luise D., "Evaluador Inteligente de C├│digo Java", CICA, 2009.