Reverse Engineering Models from Traces to Validate Distributed Systems – An Industrial Case Study

Ulrich, Andreas; Petrenko, Alexandre

doi:10.1007/978-3-540-72901-3_14

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…• Maintenance and evolution concerns: maintaining the architecture intact from analysis to implementation [25], evolution of legacy systems [12], concerns over software method and tool obsolescence [5], verification of system by producing models from traces [28] and that PIMs have long lifespan [14].…”

Section: Where and Why Is Mde Applied (Rq1)?mentioning

confidence: 99%

Where Is the Proof? - A Review of Experiences from Applying MDE in Industry

Mohagheghi

Dehlen

2008

Lecture Notes in Computer Science

166

114

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Abstract. Model-Driven Engineering (MDE) has been promoted as a solution to handle the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks. However, few efforts have been made at collecting evidence to evaluate its benefits and limitations, which is the subject of this review. We searched several publication channels in the period 2000 to June 2007 for empirical studies on applying MDE in industry, which produced 25 papers for the review. Our findings include industry motivations for investigating MDE and the different domains it has been applied to. In most cases the maturity of third-party tool environments is still perceived as unsatisfactory for large-scale industrial adoption. We found reports of improvements in software quality and of both productivity gains and losses, but these reports were mainly from small-scale studies. There are a few reports on advantages of applying MDE in larger projects, however, more empirical studies and detailed data are needed to strengthen the evidence. We conclude that there is too little evidence to allow generalization of the results at this stage.

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Section: Where and Why Is Mde Applied (Rq1)?mentioning

confidence: 99%

Where Is the Proof? - A Review of Experiences from Applying MDE in Industry

Mohagheghi

Dehlen

2008

Lecture Notes in Computer Science

166

114

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“…Although the learned models are partial and their precision depends upon the techniques of finding effective counterexamples, Ulrich and Petrenko remarked that partial models can still be used in reasoning about the developed system and its behaviour. This paper extends this remark by claiming that partial models of multiple components can help in their iterative refinements and thus lead towards finding more integration faults in the system.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…In most cases, a learned model appears to be a partial representation of the complete behavioural spectrum of a black‐box component. However, partial models are generally acceptable to carry out the validation activities in practice . The rationale behind the feedback loop is that if the tests find discrepancies between the partially learned model and the real component, it is likely that the tests exercise a new behaviour of the component that was not revealed so far .…”

Section: Approachmentioning

confidence: 99%

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

Shahbaz

Groz

2013

Software Testing Verif & Rel

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SUMMARYFrom 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. Copyright © 2013 John Wiley & Sons, Ltd.

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“…However, as explained before, Promela is not a suitable target for real-time systems since it does not have a notion of quantitative time. Ulrich & Petrenko (2007) describe a method that synthesizes models from traces of a UMTS radio network. The traces are based on test case executions and record the messages exchanged between network nodes.…”

Section: Timing Analysis With Model Checkingmentioning

confidence: 99%