mirror of
https://github.com/LCTT/TranslateProject.git
synced 2024-12-23 21:20:42 +08:00
translated
This commit is contained in:
geekpi
parent
18ce01c50b
commit
4ca9b45d73
@ -1,59 +0,0 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (geekpi)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (AI system analyzes code similarities, makes progress toward automated coding)
|
||||
[#]: via: (https://www.networkworld.com/article/3570389/ai-system-analyzes-code-similarities-makes-progress-toward-automated-coding.html)
|
||||
[#]: author: (Patrick Nelson https://www.networkworld.com/author/Patrick-Nelson/)
|
||||
|
||||
AI system analyzes code similarities, makes progress toward automated coding
|
||||
======
|
||||
Researchers from Intel, MIT and Georgia Tech are working on an AI engine that can analyze code similarities to determine what code actually does, setting the stage for automated software writing.
|
||||
Monsitj / Getty Images
|
||||
|
||||
With the rapid advances in artificial intelligence (AI), are we getting to the point when computers will be smart enough to write their own code and be done with human coders? New research suggests we might be getting closer to that milestone.
|
||||
|
||||
Researchers from MIT and Georgia Tech teamed with Intel to develop an AI engine, dubbed Machine Inferred Code Similarity (MISIM), that's designed to analyze software code and determine how it's similar to other code. What's most interesting is the potential for the system to learn what bits of code do, and then use that intelligence to change how software is written. Ultimately, a human could explain what it wants a software program to do, and then a machine programming (MP) system could come up with a coded app to accomplish it.
|
||||
|
||||
**READ MORE:** [How AI can create self-driving data centers][1]
|
||||
|
||||
"When fully realized, MP will enable everyone to create software by expressing their intention in whatever fashion that's best for them, whether that's code, natural language or something else," said Justin Gottschlich, principal scientist and director/founder of machine programming research at Intel, in the company's [press release][2]. "That's an audacious goal, and while there's much more work to be done, MISIM is a solid step toward it."
|
||||
|
||||
### How it works
|
||||
|
||||
Neural networks give similarity scores to snippets of code "based on the jobs they are designed to carry out," Intel explains. Two code samples may look completely different but be rated the same because they perform the same function, for example. The algorithm can then determine which code snippet is more efficient.
|
||||
|
||||
Primitive versions of code-similarity systems are used in plagiarism detection, for example. With MISIM, however, the algorithm looks at chunks of code and attempts to ascertain contextually whether the snippets have similar characteristics or are aiming for similar objectives. It can then offer improvements in performance, for example, or general efficiency.
|
||||
|
||||
What's critical with MISIM is the intent of the creator, and it marks an advancement towards intent-based programming, which could enable software to be designed based on what a non-programmer creator wants to achieve. With intent-based programming, an algorithm draws on a pool of open source code rather than relying on the traditional, manual method of compiling a series of step-like programming instructions, line-by-line, telling a computer how to do something.
|
||||
|
||||
"A core differentiation between MISIM and existing code-similarity systems lies in its novel context-aware semantic structure (CASS), which aims to lift out what the code actually does. Unlike other existing approaches, CASS can be configured to a specific context, allowing it to capture information that describes the code at a higher level. CASS can provide more specific insight into what the code does rather than how it does it," Intel explains.
|
||||
|
||||
This is accomplished without a compiler (a stage used in programming that converts human-readable code into the computer program). Conveniently, partial snippets can be executed just to see what happens in that piece of code. Plus, the system gets rid of some of the more tedious parts of software development, like line-by-line bug finding. More details are available in the group's paper ([PDF][3])
|
||||
|
||||
Intel says the team's MISIM system is 40-times more accurate identifying similar code than previous code similarity systems.
|
||||
|
||||
Heres_your_sign, a Redditor [commenting on blog coverage of MISIM][4], amusingly points out that thankfully the computers aren't writing the requirements too. That would be asking for trouble, the Redditor believes.
|
||||
|
||||
Join the Network World communities on [Facebook][5] and [LinkedIn][6] to comment on topics that are top of mind.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.networkworld.com/article/3570389/ai-system-analyzes-code-similarities-makes-progress-toward-automated-coding.html
|
||||
|
||||
作者：[Patrick Nelson][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.networkworld.com/author/Patrick-Nelson/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://www.networkworld.com/article/3568354/how-ai-can-create-self-driving-data-centers.html
|
||||
[2]: https://newsroom.intel.com/news/intel-mit-georgia-tech-machine-programming-code-similarity-system/#gs.d8qd40
|
||||
[3]: https://arxiv.org/pdf/2006.05265.pdf
|
||||
[4]: https://www.reddit.com/r/technology/comments/i2dxed/this_ai_could_bring_us_computers_that_can_write/
|
||||
[5]: https://www.facebook.com/NetworkWorld/
|
||||
[6]: https://www.linkedin.com/company/network-world
|
||||
@ -0,0 +1,55 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (geekpi)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (AI system analyzes code similarities, makes progress toward automated coding)
|
||||
[#]: via: (https://www.networkworld.com/article/3570389/ai-system-analyzes-code-similarities-makes-progress-toward-automated-coding.html)
|
||||
[#]: author: (Patrick Nelson https://www.networkworld.com/author/Patrick-Nelson/)
|
||||
|
||||
AI 系统分析代码相似性，向自动化编码迈进
|
||||
======
|
||||
来自 Intel、MIT 和佐治亚理工学院的研究人员正在研究一个 AI 引擎，它可以分析代码的相似性，以确定代码的实际作用，为自动化软件编写奠定基础。
|
||||
|
||||
随着人工智能 （AI） 的快速发展，我们是否会进入计算机智能到足以编写自己的代码并和人类一起完成工作？新的研究表明，我们可能正在接近这个里程碑。
|
||||
|
||||
来自 MIT 和佐治亚理工学院的研究人员与 Intel 合作开发了一个人工智能引擎，被称为机器推断代码相似性（MISIM），它旨在分析软件代码并确定它与其他代码的相似性。最有趣的是，该系统有学习代码的潜力，然后利用这种智能来改变软件的编写方式。最终，人们可以解释希望程序做什么，然后机器编程 （MP） 系统可以拿出一个已经编写完的应用。
|
||||
|
||||
Intel 首席科学家兼机器编程研究总监/创始人 Justin Gottschlich 在该公司的[新闻稿][2]中说：“当完全实现时，MP 能让每个人都能以任何最适合自己的方式，无论是代码、自然语言还是其他东西，来表达自己的意图来创建软件。这是一个大胆的目标，虽然还有很多工作要做，但 MISIM 是朝着这个目标迈出的坚实一步。"
|
||||
|
||||
### 它是如何工作的
|
||||
|
||||
Intel 解释说，神经网络”根据它们被设计执行的作业“给代码片段打出相似度分数。例如，两个代码样本可能看起来完全不同，但由于它们执行相同的功能，因此被评为相同。然后，该算法可以确定哪个代码片段更有效率。
|
||||
|
||||
例如，代码相似性系统的原始版本被用于抄袭检测。然而，有了 MISIM，该算法会查看代码块，并试图根据上下文确定这些代码段是否具有相似的特征，或者是否有相似的目标。然后，它可以提供例如性能方面的改进，或者常规效率。
|
||||
|
||||
MISIM 的关键是创造者的意图，它标志着向基于意图的编程的进步，它可以使软件的设计基于非程序员创造者想要实现的目标。通过基于意图的编程，算法会借助于一个开源代码池，而不是依靠传统的、手工的方法，编译一系列类似于步骤的编程指令，逐行告诉计算机如何做某件事。
|
||||
|
||||
Intel 解释说：”MISIM 与现有代码相似性系统的核心区别在于其新颖的上下文感知语义结构 （CASS），其目的是将代码的实际作用提炼出来。与其他现有的方法不同，CASS 可以根据特定的上下文进行配置，使其能够捕捉到更高层次的代码描述信息。CASS 可以更具体地洞察代码的作用，而不是它是如何做的。“
|
||||
|
||||
这是在没有编译器（编程中的一个阶段，将人类可读代码转换为计算机程序）的情况下完成的。方便的是，部分片段可以被执行，只是为了看看那段代码中会发生什么。另外，该系统摆脱了软件开发中一些比较繁琐的部分，比如逐行查找错误。更多细节可以在该小组的论文（[PDF][3]） 中找到。
|
||||
|
||||
Intel 表示，该团队的 MISIM 系统比之前的代码相似性系统识别相似代码的准确率高 40 倍。
|
||||
|
||||
一个 Redditor，Heres_your_sign [对 MISIM 报道][4]的评论中有趣地指出，幸好计算机不写需求。这位 Redditor 认为，那是自找麻烦。
|
||||
|
||||
加入 [Facebook][5] 和 [LinkedIn][6] 上的 Network World 社区，对热门的话题进行评论。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.networkworld.com/article/3570389/ai-system-analyzes-code-similarities-makes-progress-toward-automated-coding.html
|
||||
|
||||
作者：[Patrick Nelson][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[geekpi](https://github.com/geekpi)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.networkworld.com/author/Patrick-Nelson/
|
||||
[b]: https://github.com/lujun9972
|
||||
[2]: https://newsroom.intel.com/news/intel-mit-georgia-tech-machine-programming-code-similarity-system/#gs.d8qd40
|
||||
[3]: https://arxiv.org/pdf/2006.05265.pdf
|
||||
[4]: https://www.reddit.com/r/technology/comments/i2dxed/this_ai_could_bring_us_computers_that_can_write/
|
||||
[5]: https://www.facebook.com/NetworkWorld/
|
||||
[6]: https://www.linkedin.com/company/network-world
|
||||
Loading…
Reference in New Issue
Block a user