Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Analysis and testing of black-box component based systems by inferring partial models

Muzammil Shahbaz Roland Groz 1 
1 VASCO - Validation de Systèmes, Composants et Objets logiciels
LIG - Laboratoire d'Informatique de Grenoble
Abstract : From experience in component-based software engineering, it is known that the integration of high-quality components may not yield high-quality software systems. It is difficult to evaluate all possible interactions between the components in the system to uncover inter-component misfunctions. The problem is even harder when the components are used without source code, specifications or formal models. Such components are called black boxes in literature. This paper presents an iterative approach of combining model learning and testing techniques for the formal analysis of a system of black-box components. In the approach, individual components in the system are learned as finite state machines that (partially) model the behavioural structure of the components. The learned models are then used to derive tests for refining the partial models and/or finding integration faults in the system. The approach has been applied on case studies that have produced encouraging results.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/hal-00974760
Contributor : Catherine Oriat Connect in order to contact the contributor
Submitted on : Monday, April 7, 2014 - 2:15:48 PM
Last modification on : Sunday, June 26, 2022 - 9:35:24 AM

Links full text

Identifiers

Collections

Citation

Muzammil Shahbaz, Roland Groz. Analysis and testing of black-box component based systems by inferring partial models. Journal of : Software Testing, Verification and Reliability, Wiley, 2014, 24 (4), ⟨10.1002/stvr.1491⟩. ⟨hal-00974760⟩

Share

Metrics

Record views

213