Metrics Blog

• Mitigating the impact of mislabeled data on deep predictive models: an empirical study of learning with noise approaches in software engineering tasks April 8, 2024
  Mitigating the impact of mislabeled data on deep predictive models: an empirical study of learning with noise approaches in software engineering tasks | Automated Software Engineering (springer.com) Labelling data, annotating images or text is a really tedious work. I don’t do it a lot, but when I do it, it takes time. This paper presents […]
  Miroslaw Staron
• Sketches to models… January 28, 2024
  https://www.computer.org/csdl/proceedings-article/models/2023/248000a173/1SOLExN0XaU It’s been a while since I worked with models and I looked a bit at how things have evolved. As I remember, one of the major problems with modelling was one of its broken promises – simplicity. The whole idea with modelling was to be able to sketch things, discuss candidate solutions and then […]
  Miroslaw Staron
• Modelling digital twins… January 21, 2024
  https://www.computer.org/csdl/proceedings-article/models/2023/248000a013/1SOLEPphpHa Digital twins are becoming increasingly important. They provide a possibility to monitor their real twin without the need for costly measurements and sending technicians to the site where the real twin is located. However, development of them is not so easy and is almost one-off for every twin pair. The paper “A Model-driven Approach […]
  Miroslaw Staron
• Generating documentation from notebooks December 15, 2023
  https://github.com/jyothivedurada/jyothivedurada.github.io/blob/main/papers/Cell2Doc.pdf Understanding code is the same regardless if it is in a Jupyter notebook or if it is in another editor. Comments and documentation is the key. I try to teach that to my students and, some of them at least, appreciate it. Here is a paper that can change this to the better without […]
  Miroslaw Staron
• Log files and anomalies, once again… December 8, 2023
  https://arxiv.org/pdf/2308.09324.pdf I’ve written about log files a while back, but I think I’m getting hooked up onto the topic. It is actually quite interesting how to use it in practice. So, here is one more paper from the ASE 2023 conference. This paper presents a new way to create log data that can help spot […]
  Miroslaw Staron
• Vulnerability detection – addressing the #1 problem December 1, 2023
  https://arxiv.org/pdf/2308.10523.pdf One of the major issues with vulnerability detection in source code is the unbalanced data. Although there is a lot of known vulnerabilities, the examples of them are rather scarce. SonarQube, as a tool, can detect only ca. 30 vulnerabilities out of over 200,000 existing ones. This paper is about making the job of […]
  Miroslaw Staron
• Libraries and security November 24, 2023
  https://arxiv.org/pdf/2309.11021.pdf I often use python because of the large ecosystem of libraries. Thanks to these libraries, I do not have to focus on the details of the implementation, but I can focus on the task at hand. However, not all libraries are good, and therefore this paper captured my attention. The study aims to understand […]
  Miroslaw Staron
• Understanding log files… November 17, 2023
  https://ginolzh.github.io/papers/ASE2023_Log_Message_Readability.pdf Debugging and testing often require analyses of log files. This means that we need to read a lot of lines of information that can be useful, but at the same time it is difficult to parse it. Therefore, this paper is of interest for those who must read these files once in a while. […]
  Miroslaw Staron
• Robustness in language interpretation using LLMs November 10, 2023
  https://arxiv.org/pdf/2309.10644.pdf I’ve used language models for a while now. They are capable of many tasks, but one of their main problem is the robustness of the results. The models can produce very different results if we change only a minor detail. This paper addresses the challenge of interpretability in deep learning models used for source […]
  Miroslaw Staron
• Differential prompting for test case generation November 5, 2023
  https://arxiv.org/pdf/2304.11686.pdf Generating test cases is one of the new areas where ChatGPT is gaining traction. It is a good thing as it allows software developers to quickly raise quality of their software. This paper discusses the problem and challenges in finding failure-inducing test cases, the potential of using LLMs for software engineering tasks, and the […]
  Miroslaw Staron