Amazon’s Swe-Polybench has revealed the dirty secret of your artificial intelligence coding assistant

Amazon web services Day Swe-PolybenchA comprehensive multi -language standard designed to evaluate the artificial intelligence coding assistants through a variety of programming languages ​​and real world scenarios. the standard It addresses important restrictions in current evaluation frameworks and provides researchers and developers new ways to assess the effectiveness of artificial intelligence agents in mobility in the complex code bases.

“They now have a standard that they can evaluate to assess whether coding agents are able to solve the complex programming tasks,” he said. Anoph DurasDirector of Applied Sciences for AI applications and developers’ experiences in AWS, in an interview with Venturebeat. “The real world provides you with more complex tasks. In order to fix an error or build features, you need to touch multiple files, rather than one file.”

This version comes at a time when the coding tools that operate with the same Amnesty International exploded in popularity, with the integration of major technology companies into development environments and independent products. While these tools show impressive potential, the evaluation of their performance has been a challenge – especially through different programming languages ​​and the complexity of various tasks.

Swe-Polybench It contains more than 2000 challenges for coordinated coding from real GitHub issues that span four languages: Java (165 mission), Javascript (1,017 mission), Typescript (729 mission), and Python (199 mission). The standard also includes a class of 500 Swe-Polybench500 versions designed for faster experimentation.

“The diversity of tasks and the diversity of programming languages ​​was missing,” Duras explained about the current standards. “In Swe-Bench today, there is only one programming language, Python, and there is one task: error repairs. In polybench, unlike Swe-Bench, we have expanded this standard to include three additional languages.”

The new standard addresses restrictions directly in Beach seatsWhich has emerged as an actual standard to evaluate the coding agent with more than 50 leaders. Despite its pioneering role, SWE-Bench focuses only on Python warehouses, and is often characterized by errors stabilization tasks, and it tends greatly towards one code base-the Django warehouse represents more than 45 % of all tasks.

“On the intention, we decided to have a little representation of Java Script and Typescript, because we have a SWE engine that has already Bethon’s tasks,” Duras noted. “So instead of acting on Python, we have made sure that we have enough offers for JavaScript and Typescript in addition to Java.”

Why don’t you tell the scripts/simple failure the entire story about the performance of artificial intelligence coding

Main innovation in Swe-Polybench It is the provision of the most advanced evaluation measures beyond the traditional “success rate”, which simply measures whether the corrected correction successfully solves the coding problem.

“These coding factors were rated primarily through the scale called the success rate,” said Diras. “In short, the rate of success, is basically just a percentage of the tasks that have succeeded in applying the correction produced by agents. But this number is a very high and society.

The new metrics include localizing the file level, which evaluates the agent’s ability to determine files that need to be modified within a warehouse, and a retrieval at the level of the sentence tree (CST), which evaluates the extent of the worker’s accuracy that can determine the accurate specific symbol structures that require changes.

“In addition to the success rate, we have accuracy and summons. In order to reach the measure of accuracy and summons, we are looking at the program analysis tool called the concrete sentence building tree,” Duras explained. “It tells you how your basic file structure is formed, so that you can look at the separation knot, and within that category, what is the contract and variables.”

How Bethon remains dominant while complex tasks offer artificial intelligence restrictions

Amazon evaluation of many open source coding factors on Swe-Polybench on several patterns. Python remains the most powerful language for all the factors that have been tested, probably because of its spread in the current training and standards data. Performance deteriorates with an increase in the complexity of the task, especially when the adjustments need to three or more files.

Various agents show varying strengths across tasks. Although the performance of the tasks of installing errors is relatively fixed, there is a greater contrast between agents when dealing with advantages and re -creation of the code.

The criterion also found that informational data data greatly affects success rates, indicating that the descriptions of the clear issue are still decisive to active help in artificial intelligence.

What does Swe-Polybench mean for enterprise developers who work through multiple languages

Swe-Polybench It reaches a critical turn in developing artificial intelligence assistants. With these tools from experimental production environments to production environments, the need for strict, varied and active standards intensified.

“Over time, LLMS capabilities not only evolved, but at the same time, the tasks have become more complicated.” “Developers are needed to solve more tasks more complicated in a simultaneous way using these factors.”

Supporting the expanded standards of standards makes it of special value for institutions’ environments where the development of polyglot is common. Java, JavaScript, Typescript and Python constantly occupy among the most popular programming languages ​​in institutions settings, making Swe-Polybench cover a large degree of real world development scenarios.

Amazon made a full Swe-Polybench framework Available to the public. The data set can be accessed EmbroideryAnd the evaluation is available on Jaytab. Dedicated Leaders It was created to track the performance of various coding factors on the index.

“We have expanded the Swe-Beck pipeline to support these additional three languages,” said Diras. “Hope is that we will be able to extract this process more in the future and extend beyond four languages, and exceed the three tasks that you talked about, so that this standard becomes more comprehensive.”

With the high temperature of the artificial intelligence coding market with offers from each major technical company, Swe-Polybench provides a decisive examination of its actual capabilities. Benchmark’s design admits that the development of software in the real world requires more than simple errors in Python-requires work through languages, understanding complex code, and addressing various engineering challenges.

For decision makers in the institution who evaluate the artificial intelligence coding tools, Swe-Polybench offers an invaluable thing: a way to separate the marketing noise from the real technical ability. After all, the real test of the artificial intelligence coding assistant is not good in simplified experimental offers, but whether he can deal with the multi-language chaotic complexity of actual software projects-developers are struggling with cosmic developers every day.

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