Exploring Retrospective Meeting Practices and the Use of Data in Agile Teams
- URL: http://arxiv.org/abs/2502.03570v1
- Date: Wed, 05 Feb 2025 19:33:53 GMT
- Title: Exploring Retrospective Meeting Practices and the Use of Data in Agile Teams
- Authors: Alessandra Maciel Paz Milani, Margaret-Anne Storey, Vivek Katial, Lauren Peate,
- Abstract summary: This study explores barriers to project data utilization, including psychological safety concerns and the disconnect between data collection and meaningful integration of data into retrospective meetings.
Our findings confirm that although teams routinely collect project data, they seldom employ it systematically during retrospectives.
- Score: 43.16629507708997
- License:
- Abstract: Retrospectives are vital for software development teams to continuously enhance their processes and teamwork. Despite the increasing availability of objective data generated throughout the project and software development processes, many teams do not fully utilize this information in retrospective meetings. Instead, they often rely on subjective data, anecdotal insights and their memory. While some literature underscores the value of data-driven retrospectives, little attention has been given to the role data can play and the challenges of effectively incorporating objective project data into these meetings. To address this gap, we conducted a survey with 19 practitioners on retrospective meeting practices and how their teams gather and use subjective and objective data in their retrospectives. Our findings confirm that although teams routinely collect project data, they seldom employ it systematically during retrospectives. Furthermore, this study provides insights into retrospective practices by exploring barriers to project data utilization, including psychological safety concerns and the disconnect between data collection and meaningful integration of data into retrospective meetings. We close by considering preliminary insights that may help to mitigate these concerns and how future research might build on our paper findings to support the integration of project data into retrospective meetings, fostering a balance between human-centric reflections and data-driven insights.
Related papers
- The Landscape of Data Reuse in Interactive Information Retrieval: Motivations, Sources, and Evaluation of Reusability [5.257245308437576]
This study investigated the data reuse practices of experienced researchers from the area of Interactive Information Retrieval (IIR) studies.
We conducted 21 semi-structured in-depth interviews with IIR researchers from varying demographic backgrounds, institutions, and stages of careers on their motivations, experiences, and concerns over data reuse.
arXiv Detail & Related papers (2024-11-23T03:15:31Z) - Data-Centric AI in the Age of Large Language Models [51.20451986068925]
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs)
We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs.
We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
arXiv Detail & Related papers (2024-06-20T16:34:07Z) - The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets [15.593796580973937]
Machine learning tools can only be as capable as the quality of the data they are trained on allows them.
We present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade.
We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets.
arXiv Detail & Related papers (2024-05-14T12:50:33Z) - Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework [1.5993707490601146]
We evaluate data practices in machine learning as data curation practices.
We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles.
arXiv Detail & Related papers (2024-05-04T16:21:05Z) - Collect, Measure, Repeat: Reliability Factors for Responsible AI Data
Collection [8.12993269922936]
We argue that data collection for AI should be performed in a responsible manner.
We propose a Responsible AI (RAI) methodology designed to guide the data collection with a set of metrics.
arXiv Detail & Related papers (2023-08-22T18:01:27Z) - Causal Scene BERT: Improving object detection by searching for
challenging groups of data [125.40669814080047]
Computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection.
These modules frequently have low expected error overall but high error on atypical groups of data due to biases inherent in the training process.
Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
arXiv Detail & Related papers (2022-02-08T05:14:16Z) - An Ethical Highlighter for People-Centric Dataset Creation [62.886916477131486]
We propose an analytical framework to guide ethical evaluation of existing datasets and to serve future dataset creators in avoiding missteps.
Our work is informed by a review and analysis of prior works and highlights where such ethical challenges arise.
arXiv Detail & Related papers (2020-11-27T07:18:44Z) - Bringing the People Back In: Contesting Benchmark Machine Learning
Datasets [11.00769651520502]
We outline a research program - a genealogy of machine learning data - for investigating how and why these datasets have been created.
We describe the ways in which benchmark datasets in machine learning operate as infrastructure and pose four research questions for these datasets.
arXiv Detail & Related papers (2020-07-14T23:22:13Z) - Provably Efficient Causal Reinforcement Learning with Confounded
Observational Data [135.64775986546505]
We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting.
We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
arXiv Detail & Related papers (2020-06-22T14:49:33Z) - A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
We propose a revised evaluation scheme for the VisDial dataset.
We measure consensus between answers generated by the model and a set of relevant answers.
We release these sets and code for the revised evaluation scheme as DenseVisDial.
arXiv Detail & Related papers (2020-04-20T13:26:45Z)
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.