30,183 research outputs found
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
NLP2Code: Code Snippet Content Assist via Natural Language Tasks
Developers increasingly take to the Internet for code snippets to integrate
into their programs. To save developers the time required to switch from their
development environments to a web browser in the quest for a suitable code
snippet, we introduce NLP2Code, a content assist for code snippets. Unlike
related tools, NLP2Code integrates directly into the source code editor and
provides developers with a content assist feature to close the vocabulary gap
between developers' needs and code snippet meta data. Our preliminary
evaluation of NLP2Code shows that the majority of invocations lead to code
snippets rated as helpful by users and that the tool is able to support a wide
range of tasks.Comment: tool demo video available at
https://www.youtube.com/watch?v=h-gaVYtCznI; to appear as a tool demo paper
at ICSME 2017 (https://icsme2017.github.io/
Simplifying Deep-Learning-Based Model for Code Search
To accelerate software development, developers frequently search and reuse
existing code snippets from a large-scale codebase, e.g., GitHub. Over the
years, researchers proposed many information retrieval (IR) based models for
code search, which match keywords in query with code text. But they fail to
connect the semantic gap between query and code. To conquer this challenge, Gu
et al. proposed a deep-learning-based model named DeepCS. It jointly embeds
method code and natural language description into a shared vector space, where
methods related to a natural language query are retrieved according to their
vector similarities. However, DeepCS' working process is complicated and
time-consuming. To overcome this issue, we proposed a simplified model
CodeMatcher that leverages the IR technique but maintains many features in
DeepCS. Generally, CodeMatcher combines query keywords with the original order,
performs a fuzzy search on name and body strings of methods, and returned the
best-matched methods with the longer sequence of used keywords. We verified its
effectiveness on a large-scale codebase with about 41k repositories.
Experimental results showed the simplified model CodeMatcher outperforms DeepCS
by 97% in terms of MRR (a widely used accuracy measure for code search), and it
is over 66 times faster than DeepCS. Besides, comparing with the
state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by
73%. We also observed that: fusing the advantages of IR-based and
deep-learning-based models is promising because they compensate with each other
by nature; improving the quality of method naming helps code search, since
method name plays an important role in connecting query and code
- …