Data validation principles have been applied recently to a railways reverse-engineering project with great success. B and ProB have demonstrated again how efficient they are when used in combination.
This project was aimed at redeveloping an IDE for Embedded Diagnosis Systems (EDS), mostly from scratch as source code and development documentation are lost. Obsolete hardware (with no direct access to the filesystem) and original IDE were used for black box testing.
Verifying railways systems covers many aspects and requires a large number of cross-verifications, performed by a wide range of actors including the designer of the system, the company in charge of its exploitation, the certification body, etc. Even if complete automation is not possible, any automatic verification is welcome as it helps to improve the overall level of confidence.
Indeed a railways system is a collection of highly dependent sub-system specification and these dependencies need to be checked. They may be based on railways signalling rules (that are specific to every country or even every company in a single country), on rolling stock features (constant or variable train size or configuration) and exploitation conditions.
According to wikipedia, «in computer science, data validation is the process of ensuring that a program operates on clean, correct and useful data. It uses routines, often called validation rules or check routines, that check for correctness, meaningfulness, and security of data that are input to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic ».
In other words, the data are checked against a data model. The data model could be simple or elaborated : it is up to the human validator to define to which extent the data have to be validated.
Often data validation is understood as validating numbers, dates and text, i.e. verifying types and ranges. For that purpose, spreadsheet dedicated function can do the job. However in a number of applications the data model could be quite complex and verification difficult to automate, hence leaving room for human errors during validation.
For digital photographers, “data validation generally addresses three questions. Is the archive complete? Did the files transfer properly? And, are the files uncorrupted?“. This interpretation is out of the scope of this article.
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