Code intelligence tools such as GitHub Copilot have begun to bridge the gap between natural language and programming language. A frequent software development task is the management of technical debts, which are suboptimal solutions or unaddressed issues which hinder future software development. Developers have been found to “self-admit” technical debts (SATD) in software artifacts such as source code comments. Thus, is it possible that the information present in these comments can enhance code generative prompts to repay the described SATD? Or, does the inclusion of such comments instead cause code generative tools to reproduce the harmful symptoms of described technical debt? Does the modification of SATD impact this reaction? Despite the heavy maintenance costs caused by technical debt and the recent improvements of code intelligence tools, no prior works have sought to incorporate SATD towards prompt engineering. Inspired by this, this paper contributes and analyzes a dataset consisting of 36,381 TODO comments in the latest available revisions of their respective 102,424 repositories, from which we sample and manually generate 1,140 code bodies using GitHub Copilot. Our experiments show that GitHub Copilot can generate code with the symptoms of SATD, both prompted and unprompted. Moreover, we demonstrate the tool’s ability to automatically repay SATD under different circumstances and qualitatively investigate the characteristics of successful and unsuccessful comments. Finally, we discuss gaps in which GitHub Copilot’s suc-cessors and future researchers can improve upon code intelligence tasks to facilitate AI-assisted software maintenance

Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algorithms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity.

https://export.arxiv.org/pdf/2311.12019v2.pdf

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software

Maintainability and Scalability in Machine Learning: Challenges and Solutions

Today’s software is bloated leading to signiﬁcant resource wastage. This bloat is prevalent across the entire software stack, from the operating system, all the way to software backends, frontends, and web-pages. In this paper, we study how prevalent bloat is in machine learning containers. We develop MMLB, a framework to analyze bloat in machine learning containers, measuring the amount of bloat that exists on the container and package levels. Our tool quantiﬁes the sources of bloat and removes them. We integrate our tool with vulnerability analysis tools to measure how bloat affects container vulnerabilities. We experimentally study 15 machine learning containers from the ofﬁcial Tensorﬂow, Pytorch, and NVIDIA container registries under different tasks, (i.e., training, tuning, and serving). Our ﬁndings show that machine learning containers contain bloat encompassing up to 80% of the container size. We ﬁnd that debloating machine learning containers speeds provisioning times by up to 3 . 7 × and removes up to 98% of all vulnerabilities detected by vulnerability analysis tools such as Grype. Finally, we relate our results to the larger discussion about technical debt in machine learning systems.

Machine Learning Containers are Bloated and Vulnerable

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged by the developers and needs additional work or resources to address in the future. In recent years, though many methods have been proposed to detect SATDs, these methods have mainly focused on Java-type code comments published by Maldonado et al. It is unclear whether these methods trained on Maldonado's code comments dataset can find SATD in other programming languages or other software artifacts, such as issue trackers, pull requests, and commit messages effectively. In order to answer the above confusion and investigate how far our community has progressed in the field of SATD identification, we first collect a comprehensive dataset that contains SATDs in code comments from four different programming languages (java, python, docker file, XML) and SATDs in other different artifacts (issue tracker, pull requests, commit messages) from previous papers working in the field of SATD. Then, we re-train the existing models with Maldonado's code comments dataset and test all the models on other programming languages and other artifacts. The results show that existing SATD identification methods can find SATDs in other non-Java languages, but perform poorly in identifying SATDs from three other different artifacts. In addition, in order to simultaneously identify four different artifacts of SATDs, we develop a Multi-Task Learning model utilizing BERT for SATD identification (MT-BERT-SATD). Considering four different artifacts and the SATD identification tasks, MT-BERT-SATD achieves an average F1-score of 0.712 (0.625-0.859), which is superior to existing models from 4.6% to 30.4%. Results show that MT-BERT-SATD can effectively identify SATD instances across explored programming languages and software artifacts, indicating its capability to identify SATD instances in new and unexplored programming languages and software artifacts.

Self-Admitted Technical Debts Identification: How Far Are We?

In software development, the term “technical debt” (TD) is used to characterize short-term solutions and workarounds implemented in source code which may incur a long-term cost. Technical debt has a variety of forms and can thus affect multiple qualities of software including but not limited to its legibility, performance, and structure. In this paper, we have conducted a comprehensive study on the technical debts in machine learning (ML) based software. TD can appear differently in ML software by infecting the data that ML models are trained on, thus affecting the functional behavior of ML systems. The growing inclusion of ML components in modern software systems have introduced a new set of TDs. Does ML software have similar TDs to traditional software? If not, what are the new types of ML specific TDs? Which ML pipeline stages do these debts appear? Do these debts differ in ML tools and applications and when they get removed? Currently, we do not know the state of the ML TDs in the wild. To address these questions, we mined 68,820 self-admitted technical debts (SATD) from all the revisions of a curated dataset consisting of 2,641 popular ML repositories from GitHub, along with their introduction and removal. By applying an open-coding scheme and following upon prior works, we provide a comprehensive taxonomy of ML SATDs. Our study analyzes ML SATD type organizations, their frequencies within stages of ML software, the differences between ML SATDs in applications and tools, and quantifies the removal of ML SATDs. The findings discovered suggest implications for ML developers and researchers to create maintainable ML systems.

/pdf/23-shades-of-self-admitted-technical-debt-an-empirical-study-11n25q65.pdf

23 shades of self-admitted technical debt: an empirical study on machine learning software

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23 shades of self-admitted technical debt: an empirical study on machine learning software