90,275 research outputs found
Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk
Background. Test resources are usually limited and therefore it is often not
possible to completely test an application before a release. To cope with the
problem of scarce resources, development teams can apply defect prediction to
identify fault-prone code regions. However, defect prediction tends to low
precision in cross-project prediction scenarios.
Aims. We take an inverse view on defect prediction and aim to identify
methods that can be deferred when testing because they contain hardly any
faults due to their code being "trivial". We expect that characteristics of
such methods might be project-independent, so that our approach could improve
cross-project predictions.
Method. We compute code metrics and apply association rule mining to create
rules for identifying methods with low fault risk. We conduct an empirical
study to assess our approach with six Java open-source projects containing
precise fault data at the method level.
Results. Our results show that inverse defect prediction can identify approx.
32-44% of the methods of a project to have a low fault risk; on average, they
are about six times less likely to contain a fault than other methods. In
cross-project predictions with larger, more diversified training sets,
identified methods are even eleven times less likely to contain a fault.
Conclusions. Inverse defect prediction supports the efficient allocation of
test resources by identifying methods that can be treated with less priority in
testing activities and is well applicable in cross-project prediction
scenarios.Comment: Submitted to PeerJ C
Towards Guidelines for Preventing Critical Requirements Engineering Problems
Context] Problems in Requirements Engineering (RE) can lead to serious
consequences during the software development lifecycle. [Goal] The goal of this
paper is to propose empirically-based guidelines that can be used by different
types of organisations according to their size (small, medium or large) and
process model (agile or plan-driven) to help them in preventing such problems.
[Method] We analysed data from a survey on RE problems answered by 228
organisations in 10 different countries. [Results] We identified the most
critical RE problems, their causes and mitigation actions, organizing this
information by clusters of size and process model. Finally, we analysed the
causes and mitigation actions of the critical problems of each cluster to get
further insights into how to prevent them. [Conclusions] Based on our results,
we suggest preliminary guidelines for preventing critical RE problems in
response to context characteristics of the companies.Comment: Proceedings of the 42th Euromicro Conference on Software Engineering
and Advanced Applications, 201
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