Cristina Ribeiro
Five Years of Teaching Experience – Mentoring & Coaching – NSERC & Google Scholarships – Manager & Leader
Award-winning, highly-accomplished, and inspirational Professor of Software Engineering & Wireless and Mobile Networks with extensive theoretical and practical, progressive experience delivering inspiring teaching programs, developing curriculum, and mentoring students to improve their perceptions, awareness, and imagination. Talented classroom instructor and workshop facilitator with dynamic communication skills and motivational teaching style. A motivational leader, recognized for initiative, performing above expectations and analytical thinking. An innate ability and drive to educate and inspire, coupled with a proven track record in collaborating with students, education coordinators, and other academic staff to create a creative and technologically advanced learning environment. Outstanding research and project management attributes, with a seasoned ability to explain complex problems to a range of audiences, as well as influence, and persuade.
Supervisors: Daniel Berry, University of Waterloo, Alexander Ferworn, Ryerson University, Mieso Denko, University of Guelph, and Dave Mason, Ryerson University
Award-winning, highly-accomplished, and inspirational Professor of Software Engineering & Wireless and Mobile Networks with extensive theoretical and practical, progressive experience delivering inspiring teaching programs, developing curriculum, and mentoring students to improve their perceptions, awareness, and imagination. Talented classroom instructor and workshop facilitator with dynamic communication skills and motivational teaching style. A motivational leader, recognized for initiative, performing above expectations and analytical thinking. An innate ability and drive to educate and inspire, coupled with a proven track record in collaborating with students, education coordinators, and other academic staff to create a creative and technologically advanced learning environment. Outstanding research and project management attributes, with a seasoned ability to explain complex problems to a range of audiences, as well as influence, and persuade.
Supervisors: Daniel Berry, University of Waterloo, Alexander Ferworn, Ryerson University, Mieso Denko, University of Guelph, and Dave Mason, Ryerson University
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Papers by Cristina Ribeiro
requirements ambiguity, recent studies have indicated that requirements ambiguity
seems to be resolved through multiple inspections and discussions that characterize
the requirements engineering process. However, this process may not catch ambiguity
types that are likely to result in subconscious disambiguation. People are likely
unaware of and incapable of recognizing these ambiguity types; therefore, these
types are likely to remain after multiple inspections. This kind of ambiguity is defined
as persistent ambiguity and may cause expensive damage. The potential impact
of persistent ambiguity was investigated.
Initially, a comprehensive ambiguity model based on linguistic ambiguity and its
application to requirements engineering was developed. The model was subsequently
analyzed to determine the ambiguity types likely to result in subconscious
disambiguation and therefore likely to persist. Three requirements specifications
were inspected for instances of persistent ambiguity as defined in the model. Each
chief requirements engineer verified whether the persistent ambiguities likely to
have the greatest impact on each project were indeed interpreted ambiguously, and
if so, what the impact was.
For the three requirements specifications inspected, there is an average of one persistent
ambiguity for every 15.38 pages; project one has the highest average of one
persistent ambiguity for every 3.33 pages, project three has an average of one persistent
ambiguity for every 31.25 pages, and project two has the lowest average of
one persistent ambiguity for every 56 pages. For the three projects, none of the persistent ambiguities reviewed by each chief requirements engineer caused expensive
damage because all of the requirements engineers seemed to subconsciously disambiguate the ambiguities in the same way. For the three projects analyzed and the
ambiguities reviewed by each chief requirements engineer, the least expensive approach
would have been to forego initially identifying persistent ambiguity in these
three projects.
The first main conclusion is that persistent ambiguity remained undetected by the
teams of requirements engineers. The second main conclusion is that the process
used by these particular requirements engineering teams for these particular projects
is enough to prevent damage. The third main conclusion is that the identification
of persistent ambiguity in requirements specifications is potentially an effective
and efficient strategy for minimizing damage caused by ambiguity precisely because
of its focus on ambiguity that remained undetected due to lack of awareness. Further
study is necessary to determine what factors are involved in persistent ambiguity
and its prevalence, as well as its potential impacts.
Thesis Chapters by Cristina Ribeiro
requirements ambiguity, recent studies have indicated that requirements ambiguity
seems to be resolved through multiple inspections and discussions that characterize
the requirements engineering process. However, this process may not catch ambiguity
types that are likely to result in subconscious disambiguation. People are likely
unaware of and incapable of recognizing these ambiguity types; therefore, these
types are likely to remain after multiple inspections. This kind of ambiguity is defined
as persistent ambiguity and may cause expensive damage. The potential impact
of persistent ambiguity was investigated.
Initially, a comprehensive ambiguity model based on linguistic ambiguity and its
application to requirements engineering was developed. The model was subsequently
analyzed to determine the ambiguity types likely to result in subconscious
disambiguation and therefore likely to persist. Three requirements specifications
were inspected for instances of persistent ambiguity as defined in the model. Each
chief requirements engineer verified whether the persistent ambiguities likely to
have the greatest impact on each project were indeed interpreted ambiguously, and
if so, what the impact was.
For the three requirements specifications inspected, there is an average of one persistent
ambiguity for every 15.38 pages; project one has the highest average of one
persistent ambiguity for every 3.33 pages, project three has an average of one persistent
ambiguity for every 31.25 pages, and project two has the lowest average of
one persistent ambiguity for every 56 pages. For the three projects, none of the persistent ambiguities reviewed by each chief requirements engineer caused expensive
damage because all of the requirements engineers seemed to subconsciously disambiguate the ambiguities in the same way. For the three projects analyzed and the
ambiguities reviewed by each chief requirements engineer, the least expensive approach
would have been to forego initially identifying persistent ambiguity in these
three projects.
The first main conclusion is that persistent ambiguity remained undetected by the
teams of requirements engineers. The second main conclusion is that the process
used by these particular requirements engineering teams for these particular projects
is enough to prevent damage. The third main conclusion is that the identification
of persistent ambiguity in requirements specifications is potentially an effective
and efficient strategy for minimizing damage caused by ambiguity precisely because
of its focus on ambiguity that remained undetected due to lack of awareness. Further
study is necessary to determine what factors are involved in persistent ambiguity
and its prevalence, as well as its potential impacts.