logseq/pages/INFSOF-D-23-00572.md

type:: REVIEWS tags:: year:: 2023 venue:: IST full-title:: Review of A longitudinal study on the temporal validity of software samples due to Information and Software Technology date-start:: 19-09-2023 - 16:52 date-submitted:: external-links:: status:: DONE deadline-submission:: 20-10-2023 file::

- [[Highlights]]
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		- What's the usage of this study?
		- How could the community take advantage of it?
- ((65474ea3-1ba2-4900-98e8-c0a12b0f77d1))
	- At this stage I don't see a strong need for this study. WHat's the benefit of the results? How could I use them?
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	- What a surprise :-)
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- ![INFSOF-D-23-00572_reviewer.pdf](C:\Users\david\Nextcloud\WORK\Referaggi\2023\10-23-IST\INFSOF-D-23-00572_reviewer.pdf)
- [[Comments]]
	- **Summary**: The paper presents a longitudinal study that was conducted on 1,991 Java projects hosted on GitHub. The study aimed to answer the following three research questions: "RQ1: Does the passage of time bring about changes in the population?"; "RQ2: How long does it take for the population to change?"; and "RQ3: How long does a project remain active?" The study found that project activity generally increased over time, with data snapshots remaining representative for approximately one year. However, the representativeness declined after two years. The growth rate of projects varied, with a significant decrease in activity observed after five years. The study also found that the probability of projects remaining actively maintained was 50% at 30 months, dropping to 14% at 161 months. Furthermore, the study observed that newer projects tend to exhibit higher activity levels and attract more contributions, indicating a strong correlation between recent activity and increased engagement metrics.
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	- **Comments**: The paper is well-written and well-organized, with appropriate justification for the selection of methodological and technical tools. However, there is a significant omission in the early sections regarding the reasoning behind the study. A detailed explanation of why the research is necessary, how it could change the current state of the art, the potential impact of the findings, and the practical applications of the results is missing. Expanding on these aspects would greatly enhance the quality and depth of the paper, providing more clarity on its contributions and relevance.
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- [[REVIEWS/Notes]]
	- From [[chatgpt]]
		- a **longitudinal study** is a research design that involves repeated observations of the same variables (e.g., people) over a period of time, which can extend from years to even decades. Unlike cross-sectional studies, which look at a particular phenomenon at a single point in time, longitudinal studies can track changes over time, providing insights into the dynamics of change and development.
		- Quality thresholds and criteria to filter opensource projects from GitHub
			- ![image.png](../assets/image_1699172588473_0.png)
			- J. A. Carruthers, J. A. Diaz-Pace, and E. A. Irrazabal, “How are software datasets constructed in Empirical Software Engineering studies? A systematic mapping study,” in 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2022, pp. 442–450, doi: 10.1109/SEAA56994.2022.00075