Negativity in Self-Admitted Technical Debt: How Sentiment Influences Prioritization
- URL: http://arxiv.org/abs/2501.01068v1
- Date: Thu, 02 Jan 2025 05:33:43 GMT
- Title: Negativity in Self-Admitted Technical Debt: How Sentiment Influences Prioritization
- Authors: Nathan Cassee, Neil Ernst, Nicole Novielli, Alexander Serebrenik,
- Abstract summary: Self-Admitted Technical Debt, or SATD, is a self-admission of technical debt present in a software system.
About a quarter of descriptions of SATD in software systems express some form of negativity or negative emotions.
Our study shows how developers actively use negativity in SATD to determine how urgently a particular instance of TD should be addressed.
- Score: 50.07057212504773
- License:
- Abstract: Self-Admitted Technical Debt, or SATD, is a self-admission of technical debt present in a software system. To effectively manage SATD, developers need to estimate its priority and assess the effort required to fix the described technical debt. About a quarter of descriptions of SATD in software systems express some form of negativity or negative emotions when describing technical debt. In this paper, we report on an experiment conducted with 59 respondents to study whether negativity expressed in the description of SATD \textbf{actually} affects the prioritization of SATD. The respondents are a mix of professional developers and students, and in the experiment, we asked participants to prioritize four vignettes: two expressing negativity and two expressing neutral sentiment. To ensure realism, vignettes were based on existing SATD. We find that negativity causes between one-third and half of developers to prioritize SATD, in which negativity is expressed as having more priority. Developers affected by negativity when prioritizing SATD are twice as likely to increase their estimation of urgency and 1.5 times as likely to increase their estimation of importance and effort for SATD compared to the likelihood of decreasing these prioritization scores. Our findings show how developers actively use negativity in SATD to determine how urgently a particular instance of TD should be addressed. However, our study also describes a gap in the actions and belief of developers. Even if 33% to 50% use negativity to prioritize SATD, 67% of developers believe that using negativity as a proxy for priority is unacceptable. Therefore, we would not recommend using negativity as a proxy for priority. However, we also recognize that developers might unavoidably express negativity when describing technical debt.
Related papers
- IHEval: Evaluating Language Models on Following the Instruction Hierarchy [67.33509094445104]
The instruction hierarchy establishes a priority order from system messages to user messages, conversation history, and tool outputs.
Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy.
We bridge this gap by introducing IHEval, a novel benchmark covering cases where instructions in different priorities either align or conflict.
arXiv Detail & Related papers (2025-02-12T19:35:28Z) - Evidence is All We Need: Do Self-Admitted Technical Debts Impact Method-Level Maintenance? [1.0377683220196874]
Self-Admitted Technical Debt (SATD) refers to the phenomenon where developers explicitly acknowledge technical debt through comments in the source code.
This paper aims to empirically investigate the influence of SATD on various facets of software maintenance at the method level.
arXiv Detail & Related papers (2024-11-21T01:21:35Z) - An Exploratory Study of the Relationship between SATD and Other Software Development Activities [13.026170714454071]
Self-Admitted Technical Debt (SATD) is a specific type of Technical Debt that involves documenting code to remind developers of its debt.
Previous research has explored various aspects of SATD, including methods, distribution, and its impact on software quality.
This study investigates the relationship between removing and adding SATD and activities such as bug fixing, adding new features, and testing.
arXiv Detail & Related papers (2024-04-02T13:45:42Z) - Automatically Estimating the Effort Required to Repay Self-Admitted
Technical Debt [1.8208834479445897]
Self-Admitted Technical Debt (SATD) is a specific form of technical debt documented by developers within software artifacts.
We propose a novel approach for automatically estimating SATD repayment effort, utilizing a comprehensive dataset.
Our findings show that different types of SATD require varying levels of repayment effort, with code/design, requirement, and test debt demanding greater effort compared to non-SATD items.
arXiv Detail & Related papers (2023-09-12T07:40:18Z) - Making Large Language Models Better Reasoners with Alignment [57.82176656663245]
Reasoning is a cognitive process of using evidence to reach a sound conclusion.
Recent studies reveal that fine-tuning LLMs on data with the chain of thought (COT) reasoning process can significantly enhance their reasoning capabilities.
We introduce an textitAlignment Fine-Tuning (AFT) paradigm, which involves three steps.
arXiv Detail & Related papers (2023-09-05T11:32:48Z) - Towards Automatically Addressing Self-Admitted Technical Debt: How Far
Are We? [17.128428286986573]
This paper empirically investigates the extent to which technical debt can be automatically paid back by neural-based generative models.
We start by extracting a dateset of 5,039 Self-Admitted Technical Debt (SATD) removals from 595 open-source projects.
We use this dataset to experiment with seven different generative deep learning (DL) model configurations.
arXiv Detail & Related papers (2023-08-17T12:27:32Z) - Depression detection in social media posts using affective and social
norm features [84.12658971655253]
We propose a deep architecture for depression detection from social media posts.
We incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme.
The inclusion of the proposed features yields state-of-the-art results in both settings.
arXiv Detail & Related papers (2023-03-24T21:26:27Z) - On the Robustness of ChatGPT: An Adversarial and Out-of-distribution
Perspective [67.98821225810204]
We evaluate the robustness of ChatGPT from the adversarial and out-of-distribution perspective.
Results show consistent advantages on most adversarial and OOD classification and translation tasks.
ChatGPT shows astounding performance in understanding dialogue-related texts.
arXiv Detail & Related papers (2023-02-22T11:01:20Z) - Analyzing the Intensity of Complaints on Social Media [55.140613801802886]
We present the first study in computational linguistics of measuring the intensity of complaints from text.
We create the first Chinese dataset containing 3,103 posts about complaints from Weibo, a popular Chinese social media platform.
We show that complaints intensity can be accurately estimated by computational models with the best mean square error achieving 0.11.
arXiv Detail & Related papers (2022-04-20T10:15:44Z) - DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt
Identification using Semi-Supervised Learning [31.13621632964345]
DebtFree is a two-mode framework based on unsupervised learning for identifying SATDs.
Our experiments on 10 software projects show that both models yield a statistically significant improvement over the state-of-the-art automated and semi-automated models.
arXiv Detail & Related papers (2022-01-25T19:21:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.