Measuring bug complexity in object oriented software system

Bugs are inevitable in any software development life cycle. Most bugs are detected and removed in the testing phase. In software, we can classify bugs into two categories: (1) bugs of different severity, from a user's perspective,(how much damage the bug does) and (2) bugs of different complexity(how much is the debugging time lag between detection and correction). Prior knowledge of bug distribution of different complexity can help project managers in allocating testing resources and tools. Various researchers have proposed models for determining the proportion of bugs present in software of different complexity but none of these models have been applied to object oriented software. In this paper, we have proposed a model that will determine the proportion of different bug complexity. The paper also suggests the suitability of the proposed model for a particular data set. We have taken two data sets based on object oriented methodology namely SQL for Python and SQuirreL SQL Client software developed under open source environment.

[1]  S. Kumar,et al.  Contributions to Hardware and Software Reliability , 1999, Series on Quality, Reliability and Engineering Statistics.

[2]  Robert V. Binder,et al.  Testing object‐oriented software: a survey , 1996 .

[3]  Min Xie,et al.  Reliability growth model for object oriented software system , 1994, Proceedings of 1994 1st International Conference on Software Testing, Reliability and Quality Assurance (STRQA'94).

[4]  Sunil Kumar Khatri,et al.  SOFTWARE RELIABILITY GROWTH MODELLING FOR ERRORS OF DIFFERENT SEVERITY USING CHANGE POINT , 2007 .

[5]  Yoshinobu Tamura,et al.  Optimisation analysis for reliability assessment based on stochastic differential equation modelling for open source software , 2009, Int. J. Syst. Sci..

[6]  Suku Nair,et al.  A Model for Software Development Effort and Cost Estimation , 1997, IEEE Trans. Software Eng..

[7]  P. K. Kapur,et al.  Measuring reliability growth of software by considering fault dependency, debugging time Lag functions and irregular fluctuation , 2010, SOEN.

[8]  Budi Arief,et al.  focus developing with open source software The Many Meanings of Open Source , 2022 .

[9]  Omar Shatnawi,et al.  Why software reliability growth modelling should define errors of different severity , 2004 .

[10]  簡聰富,et al.  物件導向軟體之架構(Object-Oriented Software Construction)探討 , 1989 .

[11]  Amrit L. Goel,et al.  Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures , 1979, IEEE Transactions on Reliability.

[12]  Mary Shaw,et al.  Empirical evaluation of defect projection models for widely-deployed production software systems , 2004, SIGSOFT '04/FSE-12.

[13]  Mark de Bruijne,et al.  Source Communities : Towards a Framework to Analyze the Relationship between Openness and Reliability , 2005 .

[14]  Shigeru Yamada,et al.  s-Shaped Software Reliability Growth Models and Their Applications , 1984, IEEE Transactions on Reliability.

[15]  V.B. Singh,et al.  A generalized reliability growth model for open source software , 2010, 2010 2nd International Conference on Reliability, Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods (ICRESH).

[16]  Ying Zhou,et al.  Open source software reliability model , 2005, ACM SIGSOFT Softw. Eng. Notes.

[17]  Sujata Khatri,et al.  A reliability growth model for object oriented software developed under concurrent distributed development environment , 2010, 2010 2nd International Conference on Reliability, Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods (ICRESH).