An example situation in which such a mapping is necessary is when defect data from a variety of projects is aggregated to build a quality model that relates some characteristics of
software development to resulting quality in terms of defects.
Our goal was to develop a unified defect categorization scheme that is
completely “backward compatible” with existing data, that
captures domain-specific elements of interest to NASA, and that
has other desirable attributes of good defect taxonomies
Performance is one of the key aspects of non-functional qualities, where performance bugs can cause significant performance degradation and lead to poor user experience.
A typical trajectory to becoming a core developer is to start communicating with other project contributors and then gradually get more involved, by earning a more central position in the project's social network.
Once again, the assumption was that the most informative terms are enough to fully describe their corresponding documents from our application point of view.
Even today, software organizations who are not at the forefront of the release engineering revolution need to consider what release practices should be adopted.
To perform our analysis of build inflation and its resulting bias, we empirically study 30 million builds of the Perl Comprehensive Perl Archive Network (CPAN)’s [5] CI environment, made between 2011 and 2016.
To reduce the burden of branches, merges and conflicts, companies have recently adopted “trunk-based development,” which eliminates most (if not all) branches below the master branch. Instead, teams directly commit to the master branch (after review).
However, if the chunk-level build fails, it is not clear which of the commits in the chunk is responsible for the breakage, and one would need to rebuild each commit in the chunk individually to identify the culprit(s).
source
Sources of numerical errors include extreme sensitivity to roundoff, floating-point exceptions such as overflow/underflow, and nonreproducibility across machines or even across runs on the same machine.
Such insights could help them decide what best practices to adopt, understand why (and when) continuous delivery is feasible, and what compromises are necessary in terms of software quality and technical debt.
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