Assessing and improving state-based class testing: A series of experiments

被引:61
|
作者
Briand, LC
Di Penta, M
Labiche, Y
机构
[1] Carleton Univ, Software Qual Engn Lab, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
[2] Univ Sannio, Res Ctr Software Technol, Dept Engn, I-82100 Benevento, Italy
基金
加拿大自然科学与工程研究理事会;
关键词
state-based testing; testing experimentation; UML statecharts; category partition;
D O I
10.1109/TSE.2004.79
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper describes an empirical investigation of the cost effectiveness of well-known state-based testing techniques for classes or clusters of classes that exhibit a state-dependent behavior. This is practically relevant as many object-oriented methodologies recommend modeling such components with statecharts which can then be used as a basis for testing. Our results, based on a series of three experiments, show that in most cases state-based techniques are not likely to be sufficient by themselves to catch most of the faults present in the code. Though useful, they need to be complemented with black-box, functional testing. We focus here on a particular technique, Category Partition, as this is the most commonly used and referenced black-box, functional testing technique. Two different oracle strategies have been applied for checking the success of test cases. One is a very precise oracle checking the concrete state of objects whereas the other one is based on the notion of state invariant (abstract states). Results show that there is a significant difference between them, both in terms of fault detection and cost. This is therefore an important choice to make that should be driven by the characteristics of the component to be tested, such as its criticality, complexity, and test budget.
引用
收藏
页码:770 / 793
页数:24
相关论文
共 50 条
  • [41] Empirical evaluations on the cost-effectiveness of state-based testing: An industrial case study
    Holt, Nina Elisabeth
    Briand, Lionel C.
    Torkar, Richard
    INFORMATION AND SOFTWARE TECHNOLOGY, 2014, 56 (08) : 890 - 910
  • [42] Improving Protocol Passive Testing through 'Gedanken' Experiments with Finite State Machines
    Kushik, Natalia
    Lopez, Jorge
    Cavalli, Ana
    Yevtushenko, Nina
    2016 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2016), 2016, : 315 - 322
  • [43] A Practical Stress Correction Method for Improving Stability of State-Based Peridynamics Based on Stress Equilibrium Equation
    Gu, Quan
    Lin, Zhe
    Wang, Lei
    Sun, Baoyin
    Pan, Jinghao
    INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2022, 22 (08)
  • [44] Automated State-based Online Testing Real-time Embedded Software with RTEdge
    Hasanain, Wafa
    Labiche, Yvan
    Gheorghe, Serban
    MODELSWARD 2015 PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, 2015, : 294 - 302
  • [45] Automated State-Based Unit Testing for Aspect-Oriented Programs: A Supporting Framework
    Badri, Mourad
    Badri, Linda
    Bourque-Fortin, Maxime
    JOURNAL OF OBJECT TECHNOLOGY, 2009, 8 (03): : 121 - 146
  • [46] On the impact of state-based model-driven development on maintainability: a family of experiments using UniMod
    Filippo Ricca
    Marco Torchiano
    Maurizio Leotta
    Alessandro Tiso
    Giovanna Guerrini
    Gianna Reggio
    Empirical Software Engineering, 2018, 23 : 1743 - 1790
  • [47] State-based modelling in hazard identification
    McCoy, SA
    Zhou, DF
    Chung, PWH
    APPLIED INTELLIGENCE, 2006, 24 (03) : 263 - 279
  • [48] State-based network similarity visualization
    Murugesan, Sugeerth
    Bouchard, Kristofer
    Brown, Jesse
    Kiran, Mariam
    Lurie, Dan
    Hamann, Bernd
    Weber, Gunther H.
    INFORMATION VISUALIZATION, 2020, 19 (02) : 96 - 113
  • [49] State-Based Model Slicing: A Survey
    Androutsopoulos, Kelly
    Clark, David
    Harman, Mark
    Krinke, Jens
    Tratt, Laurence
    ACM COMPUTING SURVEYS, 2013, 45 (04)
  • [50] State-Based Regression with Sensing and Knowledge
    Scherl, Richard
    Tran, Cao Son
    Baral, Chitta
    PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE, 2008, 5351 : 345 - +