The book explains how these models work for natural language processing, but making it work for source code is trivial. Use your code instead of the provided text and there you go. You need a GPU or use some cloud service, otherwise you will wait forever.
But if you have it, you can get really cool results within a day or two.
So, this week’s post is my reflection on the seminar that we hosted last week (the recording is above). It was an eye-opener for me in a few aspects.
For the first, it was the question of ownership of things. Since AI is not a subject in legal cases, it cannot really own anything. I know, AI and computational models are not the same, but for the sake of the argument let’s assume that they are. By the end of the day, it is still a human being that presses the button and generates new source code or comments or what have you. So, the responsibility is still very much on us when we use these tools.
The second, it was the question about the community and why we have open-source software. We certainly do not put our source code openly for someone to profit from it. Attribution and recognition are very important (if not the most important) aspects of any open-source community. So, using their code to create commercial models requires at least some attribution. Why not show which code was used to train these models and show how good the communities really are?
Finally, my main point still stands – we should use these models to become better. They make us so much more productive that we should not go back to the old ways of writing software. Providing suggestions and ideas to programmers can make our software better, shipped faster and potentially more reliable.
However, we need to make sure that we change the way we attribute the software. Myself, I will start to add “co-created by Github Co-pilot and the OSS communities” to my work when I use the tool. Maybe you can do that too? At least to give some attribution back to our countless colleagues who deserve it….
I think that almost everyone in the field of SE has heard about the new bot – ChatGPT from OpenAI. The bot is capable of writing programs, making conversations and even creating virtual machines.
So, this year I wanted to check what ChatGPT can do. Here is my conversation with it:
M: What are the most important machine learning papers from 2022?
C: I’m sorry, but I don’t have access to papers or other information from 2022 because my knowledge was cut off in 2021. I can tell you about some important machine learning papers from the past, or provide general information about machine learning and how it has evolved over time. Is there something specific you would like to know about machine learning?
M: Ok, how about the most important papers from 2021?
C: I’m sorry, but I am not able to browse the internet and do not have access to information about specific papers or other developments that have occurred since my knowledge cutoff in 2021. However, there are many resources available online that can help you learn about the latest developments in the field of machine learning. Some places you might look for information about the most important machine learning papers from 2021 include scientific journals and conference proceedings, online news sources, and blogs or websites that cover developments in the field of artificial intelligence and machine learning. Additionally, you can check out the proceedings of major machine learning conferences, such as the International Conference on Machine Learning (ICML) and the Conference on Neural Information Processing Systems (NeurIPS), which are typically held each year and showcase the latest research in the field.
M: What is your prediction for machine learning field for 2023?
C: One trend that has been especially prominent in the field of machine learning in recent years is the increasing use of deep learning techniques, which have led to significant advances in a wide range of applications, including image and speech recognition, natural language processing, and autonomous systems. It is likely that this trend will continue in the coming years, as researchers continue to develop and refine deep learning algorithms and apply them to new and challenging problems.
Another trend that is likely to continue in the field of machine learning is the increasing importance of interpretability and explainability. As machine learning models become more sophisticated and are applied to more complex and sensitive tasks, it will be important to understand how they make their predictions and decisions, and to be able to explain these to users and stakeholders. Researchers and practitioners will therefore continue to focus on developing techniques for interpreting and explaining the behavior of machine learning models.
Finally, the field of machine learning will continue to be driven by the availability of large amounts of data and the increasing power and efficiency of computing hardware. As data continues to be generated at an ever-increasing rate, and as hardware capabilities continue to improve, it is likely that machine learning techniques will continue to advance and be applied to a wide range of problems and domains.
M: Ok, then write a program that draws a Christmas tree.
Here is a Python program that uses ASCII art to draw a simple Christmas tree:
This code will output the following tree to the console:
You can customize the appearance of the tree by modifying the print statements. For example, you could add more branches to the tree by adding additional print statements, or you could use different characters to create a more detailed or decorative tree.
Here is an example of a more detailed tree that uses a combination of ASCII art and Unicode characters to create a more realistic-looking tree:
Understanding programming language is an important topic in research in the area of programming language models. I’ve written before that there are ca. 50 programming language models, which we can use in software engineering. Ok, not all of them are equivalent and they are specific to the task, but they are available, so we can use and customize them.
This article is a study done by our colleagues from the department. It’s too long to quote in detail, but there are a few things that I like. First, it’s a good overview of the types of language models:
Tree-based representation: when the program code is seen from the perspective of their Abstract-Syntax-Tree, an example is the code2vec model: code2vec
Graph-based models: when the program code is seen as a directed graph, e.g., a control flow graph
Although I like this classification, I see that it misses one of the most prominent and the most popular one – the NLP based model. It is a type of model where the program code is seen as a set of sentences that have meaning of some sort. It is a derivative of the token-based representation, but it is much more than that. CodeX from OpenAI is an example of such model.
Nevertheless, this study provides a very interesting set of examples of models and their applications. I sincerelly suggest to take a look at this paper to understand how the models work and where they are used best.
I’ve written about programming language models before, and it is no secret that I am very much into this topic. I like the way in which software engineering evolves – we become a more mature discipline and our tools become smarter by the hour (at least that’s how it feels).
This paper presents a new language model that is capable of doing code edits, i.e., such things as bug fixes. The model is essentially a transformer with an architecture that has been published before. However, the strength of this model is in the way in which it is trained. It uses so-called edit plans to train the model to change the input code, rather than to complement it.
The difference may not sound like much, but it is significant. The existing models are trained to complete code sequences and therefore they are very good in generating code. However, when given a code that does not require any generation, they tend to copy the input sequence to the output sequence. Well, not very useful that is.
Thanks to this new way of training, the model is able to edit code, remove defects, address review comments and so on. Yes, address review comments, this is not a joke. I sincerely believe that we can use this in practice in our tools one day.
Language models are powerful tools if you know how to use them. One of the areas where they can be used in recognizing security vulnerabilities. In this article, the authors look into six language models and test them.
The results show that there are more challenges than solutions in this area. The models can be applied to languages, but the problem is with the examples and the ground truth. What is good about the paper is that it provides a good overview of the models and how they are used. They also look a bit deeper on why the limitations of the models happen.
It’s something that our team has also observed in other context, but I will talk about that in some other event. Stay tuned.
As you have probably observed I’ve been into language models for code analysis, design and recognition. It’s a great way of spending your research time as it gives you the possibility to understand how we program and understand how to model that. In my personal case, this is a great complement to the empirical software engineering research that I do otherwise.
In the recent time I got a feeling that I look into more and more of these models, all of them baring certain similarity to the Google’s BERT model or the Fracebook’s TransCoder. So I set off to do a short review of the papers that actually talk about code models or, as they are often called, programming language models. I started from the paper describing CodeBERT ( [2002.08155] CodeBERT: A Pre-Trained Model for Programming and Natural Languages (arxiv.org) ) and looked at the 500 citations that the model has. The list below is just the list of the models that are created based on CodeBERT. There are also models created based on AlphaGo or Github CoPilot, but I leave these for another occasion.
I must admit that I did not read all of these papers and did not look at all of these models. Far from it, I only looked at some of them. My conclusion is that we have a lot of models, but the quality of the results vary a lot. The best models provide good results in ca. 20% of cases. AlphaCode is an example of such a model, which is fantastic, but not super-accurate all the time. As the model is used for super-competitive tasks, 20% is actually very impressive – it’s difficult to say that I would do better for these programming competitions, so I’m not criticizing.
The best model I’ve seen so far, however, is the Github CoPilot, which is by far the best model to create code that the world has seen. Well, there may be models that the world has not seen, but then they do not count. If you would like to see a preview of how I use it (part I), you can take a look at this video:
I sincerely hope that you find this list useful and that you can help me to keep it updated – drop me an e-mail about the list if you want to:
To be honest, I did not expect machine learning to be part of a compiler… I’ve done programming since I was 13, understood compilers during my second year at the university and even wrote one (well, without any ML, that is).
Why would a compiler need machine learning, I wondered. It’s a pretty simple program – it takes a grammar, then parses the source code and translates that to a machine code (or some other low level representation). It has to be deterministic as the same program cannot compile to two different machine codes. It’s just the way it is….
It turns out that machine learning is used in modern compilers to perform optimizations. The optimizations are done to take advantage of modern processors, their registers and long instructions sets. These optimizations are meant to support machine code in being more parallel, allowing the modern multi-core, multi-thread processors to utilize every little bit of energy in all their cores.
In this paper, the authors use language models like BERT to create a benchmark that will allow different optimization techniques to be compared. This means, that the same compiler, can test itself against these benchmarks in order to find the best possible solution. Clever!
However, this is it from me. I’m planning on writing a compiler, let alone an optimizer. I may use BERT models in the future for generation of programs, but I will most probably end there. But, in case you wonder – there is ML in compilers 🙂
Articla available at: https://arxiv.org/pdf/2205.11739.pdf
It’s no secret that I’ve been fascinated by modern, BERT-like language models. I’ve seen what they can do and how they operate, use them in two of my research projects. So, when this paper came around, I read it directly.
It’s a paper which makes an overview of what kind of tasks the language models are used in software engineering today. The list is long and contains a variety of tasks, e.g., code-to-code retrieval, repairing of source code or bug finding/fixing. In total a lot of these tasks, but, IMHO, a bit low-level tasks. There are no tasks that attempt to understand code at the design-level, for example whether we can really see specific design in the code.
The paper also shows which models are used, and provides references to these models. They list 20 models, with the tasks for which they were trained, including the datasets that they were trained on. Fantastic!
I need to dive deeper into these models, but I’m super happy about the fact that there is a list of these models now and that the language technology makes a significant body of work in software engineering now.
Using chatbots has gained importance in recent years, which has resulted in development of several chatbot platforms (like Amazon Lex, Google DialogFlow or IBM Watson). However, there is a limited number of studies related to quality assurance of chatbots. The paper from Pablo C. Cañizares, Sara Pérez-Soler, Esther Guerra and Juan de Lara addresses just this problem – how to guide testing of chatbots by using design metrics.
The paper proposes six global metrics (e.g., number of intents of the bot), eight intent metrics (e.g., number of training phrases per intent), three entity metrics (e.g., word length), and three flow metrics (e.g., conversation length). By measuring the values for these metrics, software designers of chatbots can predict three usability types – effectiveness, efficiency and satisfaction. To support the measurement process, the paper proposes a tool, available on GitHub, which can be used by practitioners. For some of the metrics, the tool employs machine learning and natural language processing. The metrics and the tool are evaluated on twelve chatbot designs. The tool could identify quality issues in terms of readability, conversation complexity, user experience and bot understanding. This demonstrates the usefulness of the tool in practice and how these metrics can help software developers in designing high-quality bots.