| dc.description.abstract | Software testing is widely recognized as an essential part of any software
development process, representing however an extremely expensive activity.
The overall cost of testing has been estimated at being at least half of the
entire development cost, if not more. Despite its importance, however,
recent studies showed that developers rarely test their application and most
programming sessions end without any test execution. Indeed, new methods
and tools able to better allocating the developers effort are needed in order
to increment the system reliability and to reduce the testing costs.
The resources available should be allocated effectively upon the portions
of the source code that are more likely to contain bugs. In this thesis we
focus on three activities able to prioritize the testing effort, specifically bug
prediction, test case prioritization, and detection of code smell able to fix
energy issues. Indeed, despite the effort devoted by the research community
in the last decades through the conduction of empirical studies and the
devising of new approaches led to interesting results, in the context of our
research we highlighted some aspects that might be improved and proposed
empirical investigations and novel approaches.
In the context of bug prediction, we devised two novel metrics, namely the
developer’s structural and semantic scattering. These metrics exploit
the presence of scattering changes that make developers more error-prone.
The results of our the empirical study show the superiority of our model
with respect to baselines based on product metrics and process metrics. Afterwards,
we devised a “hybrid” model providing an average improvement
in terms of prediction accuracy. Besides analyzing on predictors, we proposed
a novel adaptive prediction classifier, which dynamically recommends
the classifier able to better predict the bug-proneness of a class, based on the
structural characteristics of the class. The models based on this classifier are
able to outperform models based on stand-alone classifiers, as well as those
based on the Validation and Voting ensemble technique in the context
of within-project bug prediction. Laterly, we performed a differentiated
replication study in the contexts of cross-project and within-project bug
prediction. We analyzed the behavior of seven ensemble methods. The
results show that the problem is still far from being solved and that the use
of ensemble techniques does not provide evident benefits with respect to
stand-alone classifiers, independently from the strategy adopted to build
model. Finally, we confirmed, in the context of ensemble-based models,
the findings of previous studies that demonstrated that cross-project bug
prediction models perform worse than within-project ones, being however
more robust to performance variability.
With respect to the test case prioritization problem, we proposed a genetic
algorithm based on the hypervolume indicator. We provided an extensive
evaluation of Hypervolume-based and state-of-the-art approaches when
dealing with up to five testing criteria. Our results suggest that the test
ordering produced by HGA is more cost-effective than those produce by
state-of-the-art algorithms. Moreover, our algorithm is much more faster
and its efficiency does not decrease as the size of the software program and
of the test suite increase.
To cope with energy efficiency issues of mobile applications and thus
reducing the effort needed to test this non-functional aspect, we devised
two novel software tools. PETrA is able to extract the energy profile of
mobile applications, while aDoctor is a code smell detector able to identify
15 Android-specific code smells defined by Reimann et al.. We analyzed
the impact of these smells, by a large empirical study with the aim of
determining to what extent code smells affecting source code methods of
mobile applications influence energy efficiency and whether refactoring
operations applied to remove them directly improve the energy efficiency of
refactored methods. The results of our study highlight that methods affected
by code smells consume up to 385% more energy than methods not affected
by any smell. A fine-grained analysis reveals the existence of four specific
energy-smells. Finally, we also shed light on the usefulness of refactoring as
a way for improving energy efficiency by code smell removal. Specifically,
we found that it is possible to improve the energy efficiency of source code
methods by up to 90% through refactoring code smells.
Finally, we provide a set of open issues that should be addressed by the
research community in the future. [editd by author] | it_IT |
