Learning a Static Analyzer: A Case Study on a Toy Language
Static analyzers are meta-programs that analyze programs to detect potential errors or collect information. For example, they are used as security tools to detect potential buffer overflows. Also, they are used by compilers to verify that a program is well-formed and collect information to generate better code. In this paper, we address the following question: can a static analyzer be learned from data? More specifically, can we use deep learning to learn a static analyzer without the need for complicated feature engineering? We show that long short-term memory networks are able to learn a basic static analyzer for a simple toy language. However, pre-existing approaches based on feature engineering, hidden Markov models, or basic recurrent neural networks fail on such a simple problem. Finally, we show how to make such a tool usable by employing a language model to help the programmer detect where the reported errors are located.
The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized. While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission. Just detecting whether a variable is initialized in a string is a toy algorihtmic task, similar to the ones solved in a number of paper in recent years by models such as the Neural Turing Machine, Stack RNNs, Neural GPU, or Differentiable Neural Computer. All these architectures perform almost perfectly on a number of algorithmic tasks, so it is highly probable that they would also solve this one. Unluckily, the authors only compare to much more basic models, such as HMMs. Since the code for many of the above-mentioned models is available online, a paper without these baselines is not ready for ICLR. Moreover, there is a risk that existing models already solve this problem very well, making the contribution unclear.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning a Static Analyzer: A Case Study on a Toy Language
Static analyzers are meta-programs that analyze programs to detect potential errors or collect information. For example, they are used as security tools to detect potential buffer overflows. Also, they are used by compilers to verify that a program is well-formed and collect information to generate better code. In this paper, we address the following question: can a static analyzer be learned from data? More specifically, can we use deep learning to learn a static analyzer without the need for complicated feature engineering? We show that long short-term memory networks are able to learn a basic static analyzer for a simple toy language. However, pre-existing approaches based on feature engineering, hidden Markov models, or basic recurrent neural networks fail on such a simple problem. Finally, we show how to make such a tool usable by employing a language model to help the programmer detect where the reported errors are located.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized. While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission. Just detecting whether a variable is initialized in a string is a toy algorihtmic task, similar to the ones solved in a number of paper in recent years by models such as the Neural Turing Machine, Stack RNNs, Neural GPU, or Differentiable Neural Computer. All these architectures perform almost perfectly on a number of algorithmic tasks, so it is highly probable that they would also solve this one. Unluckily, the authors only compare to much more basic models, such as HMMs. Since the code for many of the above-mentioned models is available online, a paper without these baselines is not ready for ICLR. Moreover, there is a risk that existing models already solve this problem very well, making the contribution unclear.
assistant
The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized. While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission. Just detecting whether a variable is initialized in a string is a toy algorihtmic task, similar to the ones solved in a number of paper in recent years by models such as the Neural Turing Machine, Stack RNNs, Neural GPU, or Differentiable Neural Computer. All these architectures perform almost perfectly on a number of algorithmic tasks, so it is highly probable that they would also solve this one. Unluckily, the authors only compare to much more basic models, such as HMMs. Since the code for many of the above-mentioned models is available online, a paper without these baselines is not ready for ICLR. Moreover, there is a risk that existing models already solve this problem very well, making the contribution unclear.