  Driven by new software development processes and testing in clouds, system
and integration testing nowadays tends to produce enormous number of alarms.
Such test alarms lay an almost unbearable burden on software testing engineers
who have to manually analyze the causes of these alarms. The causes are
critical because they decide which stakeholders are responsible to fix the bugs
detected during the testing. In this paper, we present a novel approach that
aims to relieve the burden by automating the procedure. Our approach, called
Cause Analysis Model, exploits information retrieval techniques to efficiently
infer test alarm causes based on test logs. We have developed a prototype and
evaluated our tool on two industrial datasets with more than 14,000 test
alarms. Experiments on the two datasets show that our tool achieves an accuracy
of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
cause analysis. Due to the attractive experimental results, our industrial
partner, a leading information and communication technology company in the
world, has deployed the tool and it achieves an average accuracy of 72% after
two months of running, nearly three times more accurate than a previous
strategy based on regular expressions.
