Based on our experience, this section provides concrete advice about how release engineering can impact software engineering researchers who need to mine software repositories.
present
We present this advice in the form of a checklist, setting the stage with a fictional conversation between a naive professor and clever student, then discussing what is the recommended course of action in order to preserve the integrity of the research findings.
The first set of experiments aims to study
the run-time performance of SALAD in extracting method
sequences from bytecode and in training its API usage models.
So instead one must choose a set of discrete categories and devise mapping rules to convert the data consistently and coherently into the new categories.
Improving quality assurance effort in software projects is an important task to keep the existing customers satisfied and to compete in a competitive market.
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.
We then aggregate this data per period of six months to obtain the evolution of build failure ratio across time.
Thus, some sort of mapping must be done to convert the data into a common format
that still preserves as much of the meaning of the original data as possible.
sustain
To sustain such pursuit and provide a focus for the activity a process description has been found useful.
go
A natural question to ask is to what extent do performance bugs have the potential to go undetected if tested under default environments.
However,
the area of software quality measurements and quantification is beset with undue complexity and has, in some ways, advanced away from the developer.
A CI environment such as Jenkins kicks off a build multiple times a day, either for each patch currently under review, each new commit entering the version control system or at set times during the day (e.g., nightly build), with the aim of notifying developers as soon as possible of breakage [1], [2].
The goal of causal analysis is to identify the root cause of defects and initiate actions so that the source of defects is eliminated.
To reduce the burden of branches, merges and conflicts, companies have recently adopted "trunk-based development", which eliminates most branches below the master branch.
Defects4J
contains various types of defects from open source projects to eliminate the bias of the dataset [39] in terms of the defects types and the abundance of these defect classes [34, 39].
our simulation model requires only parameters for effort and size.
Given a suitable branching structure, practitioners also re- quire approaches and tools to predict the presence of integration conflicts [18, 32, 67, 90] and amount of effort required to resolve them.
most existing work assumes default configurations while ignoring the influence of various configurations.
Despite the advantages of JIT defect prediction, like all prediction models, they assume that the properties of past events (i.e., fix-inducing changes) are similar to the properties of future ones.
Maintaining a productive and collaborative team of developers is essential to Open Source Software (OSS) success, and hinges upon the trust inherent among the team.
The patches of developers need to be reviewed and integrated from developer branches and tested by the Continuous Integration system, until they land in the master branch.
When an upcoming release enters the feature-complete stage, release stabilization begins, where major bugs in the functionality of the release are addressed.
Unfortunately, the load of the CI system fluctuates across time, for example depending on the kind of features being developed or the progress of the current development sprint.
Despite being a fairly expected phenomenon, it informs us that software obfuscation at
this point is still not a general interest to mobile development.
we calculate agreement across the union of ratings of (round 1, group A) and (round 2, group B), and the union of (round 1, group B) and (round 2, group A), as these pairs of ratings consider the same comments.
This initial research question aims at under- standing how often builds fail and whether the ratio of failing builds is a constant value or fluctuates across time.
Build inflation seems to correspond to differences/similarities in OSes/environments, which can identify clusters of Perl versions/OSes with different failure ratio and value.
Build inflation seems to correspond to differences/similarities in OSes/environments, which can identify clusters of Perl versions/OSes with different failure ratio and value.
On the other hand, 11% of the environment build vectors ended up failing in the most recent Perl versions and were unable to recover, or never succeeded at all (0-0 and 1-0 patterns).
Especially if one OS is less popular than others, it might have seen less testing or some features might not have been ported over, causing test scripts to fail. Such failures would receive less weight in the build results than builds failing consistently across all OSes.
Linux causes more inconsistencies when being built with a small number of other OSes (small N ), but is surpassed by Windows (and Cygwin) in larger sets of OSes (large N).
The main take-home message is that, while release engineering technology has flourished tremendously due to industry, empirical validation of best practices and the impact of the release engineering process on (amongst others) software quality is largely missing and provides major research opportunities.
Other branches might see much higher percentages of code changes that never make it to the master branch, because they fail integration tests or reviews somewhere along the branch structure.
achive
To achieve this goal, Firefox changed its development pro- cess. First, versions are no longer supported in parallel, i.e., a new version supersedes the previous ones.
Lately, security of the release engineering pipeline in general, and the CI server in particular, also has become a major concern [9], since any malicious script capable of injecting a payload in the CI’s build output could infect the rest of the release engineering pipeline.
In general, the correctness of a build system is impacted by the way in which build specifications co-evolve with the source code, for example to include new source code files in the build.
affect
To understand whether and how transitioning to a rapid release model can affect the quality of a software system as observed by users, we empirically study the historical field testing data of Mozilla Firefox.
For example, researchers can provide value by analyzing release engineering data stored in industrial version control systems, bug/review repositories, CI servers and deployment logs, with the aim of gleaning actionable insights for release engineers.
Other factors that can impact the findings of an empirical study are the differences in scope between major, minor and patch releases, as well as the interleaving of releases.
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