Software testing hyper automatization with Artificial Intelligence
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The Traceability chronicles – Episode 1: How logs can help you understand what your users do?
We start here a series of articles explaining some of our most interesting results with regard to traceability of applications. We will address the issues of automatic code instrumentation to trace the system by itself and the underlying difficulties. We will also discuss what constitutes a good, useful, and usable application trace. We will also …
Software Traces for Detecting Frauds in BL.Enfance
Fraudulent behaviors can be detected based on the applicative traces. In this work, we used application traces from BL.Enfance, one of our software dedicated to children’s activities billing and management. We specifically focused on detecting the modification of variables such as the family quotient (Q-CAF) and period of billing, which can be modified to change …
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Data visualisation to ease planning in Infosilem Academics
The InfoSilem Academics software (previously TimeTabler) automates and simplifies the creation of academic schedules for faculties. The scheduling is centered on the notion of “deliveries”. A delivery is a combination of professors, academic blocks, and corresponding room requests, repeated at a given frequency. The software provides optimized time slots and room affectations depending on room constraints, the frequency of the delivery, …
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Analysis of user activity (traces) in software
The software for public services are always more complex as the regulation evolves constantly and the user requirement continues to refine. Thus, the quality of the software declines, making it more difficult to maintain and use. For example, some anomalies occurring in the production environment cannot be reproduced and therefore cannot be resolved. From the …
