The article introduces automated traceability tools and techniques. It discusses backward and forward traceability and provides an overview of automated traceability software solutions, including a probabilistic network model. The article presents experimental results using recall, precision, and lag metrics to analyze different techniques. It concludes by introducing best practices for automated traceability, arguing for unifying development processes and adding traceability support to third-party modeling tools.

1. The article introduces automated traceability, give overview of tools developed for
automated traceability and provides analysis of different traceability techniques.
First part of article discuses the importance and are of usage for automated
traceability . Article states that AT is supported by made standards, and there is two
main traceability techniques – backward- and forward- tractability.
In next part paper provides overview of automated tractability software solutions.
Also provides description of probabilistic network model and formula used to
calculate probability between two values query and artifact, which we trace. Then
author provides experimental data using recall metric, precision metric and lag
metric calculations . Based on this metric techniques author provides data from
different solution which work on specific subsets of Main of them are Ice-Breaker
Systems(IBS), event-based traceability, Logistics and Automation.
In final part author of the part introduces best practices used for automated
traceability. Paper argues about importance of unifying development process in order
to make traceability tools more precise. Besides ,as article states,it is important to add
support of traceability to third party modeling tools. To conclude article provides
process improvement plan technique.
Content of the article is well structured. Author stats from general definition and goes
to concrete examples. Also in article there is experimental data to support theory and
provide examples for methodologies.
On negative side I could not find reference in body of the article which are provide a
the Reference list. Author , in many cases, introduces a lot of new terms without
providing there short definition for them. In addition Figure #4 is quite complicated
and it is hard to grasp the concept behind the figure.
Some of the ides provided in the article can be used in model driven engineering fr
automotive systems. First as well as AT MDA for automotive can be used during
whole process of development not only in first or final stage . It would be good idea
to use rich trace support concept in order to provide connection between different
modules and sub-modules of vehicles. As well as AT can be to 3rd party case tools we
can add model-driven support/ API to development environments to improve
developing process. We can use test techniques similar to ones in the paper to test
developed model It would be god idea to create common terminology for model
driven enginerring fr automotive in order to fascilitate work beween difernt teams .
As car has very complex structure and some of it parts are used for many purposes it
would be good idea to use AT to trace use of specific parts and combine them in one