24,434 research outputs found
From Query to Usable Code: An Analysis of Stack Overflow Code Snippets
Enriched by natural language texts, Stack Overflow code snippets are an
invaluable code-centric knowledge base of small units of source code. Besides
being useful for software developers, these annotated snippets can potentially
serve as the basis for automated tools that provide working code solutions to
specific natural language queries.
With the goal of developing automated tools with the Stack Overflow snippets
and surrounding text, this paper investigates the following questions: (1) How
usable are the Stack Overflow code snippets? and (2) When using text search
engines for matching on the natural language questions and answers around the
snippets, what percentage of the top results contain usable code snippets?
A total of 3M code snippets are analyzed across four languages: C\#, Java,
JavaScript, and Python. Python and JavaScript proved to be the languages for
which the most code snippets are usable. Conversely, Java and C\# proved to be
the languages with the lowest usability rate. Further qualitative analysis on
usable Python snippets shows the characteristics of the answers that solve the
original question. Finally, we use Google search to investigate the alignment
of usability and the natural language annotations around code snippets, and
explore how to make snippets in Stack Overflow an adequate base for future
automatic program generation.Comment: 13th IEEE/ACM International Conference on Mining Software
Repositories, 11 page
NPEFix: Automatic Runtime Repair of Null Pointer Exceptions in Java
Null pointer exceptions, also known as null dereferences are the number one
exceptions in the field. In this paper, we propose 9 alternative execution
semantics when a null pointer exception is about to happen. We implement those
alternative execution strategies using code transformation in a tool called
NPEfix. We evaluate our prototype implementation on 11 field null dereference
bugs and 519 seeded failures and show that NPEfix is able to repair at runtime
10/11 and 318/519 failures
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
Blueprint Buffalo Action Plan: Regional Strategies for Reclaiming Vacant Properties in the City and Suburbs of Buffalo
Over a period of about nine months, the NVPC team conducted interviews and gathered insights that have resulted in this report. During the study period, Buffalo–Niagara emerged as a region broadly challenged by decades of disinvestment and population loss, but also as a close network of communities singularly blessed with a wealth of historic, transit-friendly, and affordable neighborhoods and commercial areas. Building on the City of Buffalo’s “asset management” strategy first proposed in 2004 by the Cornell Cooperative Extension Association—and now formally adopted by the Buffalo Common Council as part of its comprehensive 20-year plan for the city—the NVPC team sought to reexamine how the revitalization of Buffalo’s vacant properties could actually serve as a catalyst to address the region’s other most pressing problems: population loss, a weak real estate market in the inner city, signs of incipient economic instability in older suburbs, quality-of-life issues, school quality, and suburban sprawl
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