=Paper=
{{Paper
|id=Vol-1435/keynote
|storemode=property
|title=Negative Results in Computer Science Evaluations
|pdfUrl=https://ceur-ws.org/Vol-1435/NoISE2015_keynote.pdf
|volume=Vol-1435
|dblpUrl=https://dblp.org/rec/conf/esws/Vidal15
}}
==Negative Results in Computer Science Evaluations==
<pdf width="1500px">https://ceur-ws.org/Vol-1435/NoISE2015_keynote.pdf</pdf>
<pre>
  Negative Results in Computer Science
              Evaluations

                          Maria-Esther Vidal

                 Universidad Simón Bolı́var, Venezuela
                          mvidal@ldc.usb.ve



Abstract. In Computer Science, properties of formal theories that model
real-world phenomena can be formally demonstrated using logic formal
systems, e.g., given a proof of the best case complexity of a problem, or
a demonstration of the soundness and completeness of a solution. Ad-
ditionally, as in other Natural Sciences, characteristics of a theory can
be empirically evaluated following the scientific method which provides
procedures to systematically conduct experiments and to test hypothe-
ses about these characteristics. Formally proven properties or empirically
confirmed hypotheses can be accepted as accounting of known facts,
while falsifiable statements that cannot be validated correspond to neg-
ative and inconclusive results. In this talk, we first discuss the different
types of negative results that can be obtained during the formal and em-
pirical validation of Computer Science approaches, e.g., contra-examples
of theorems, intractability and undecidability of a problem, or statisti-
cally non significant results. Next, we analyze the reasons that may con-
duct to observe negative results, and more importantly, the relevance of
publishing negative results is discussed. Moreover, we attempt to aware
our attendees about the tendency of camouflaging negative results as
positive results by non-evaluating problematic solutions or redefining a
problem. Finally, we encourage the definition of guidelines for reporting
results that more that being seen as negative, should be considered as
new challenges that will allow for the advance of the research areas.

</pre>