Replication Package IntroductionThis is a replication package for my paper “Emerging Trends Software in Architecture Through the Practitioner Lens: A Five-Year Review”.Use/Check the replication packageRead the README.md to check the details.Preparation and installationFollow the instructions in INSTALL.md to install and configure all the tools.Using scriptCheck requirements.txt and download all the needed libs, then you can use the script directly.Results analysis for the RQsFor the raw data and search strategy, you can check in the Raw Data folder.For the LLM implementation process, you can check in the LLM Processing folder.For all scripts, you can check in the Scripts folder.For how to use the Gephi tool in our research, you can check in the Gephi folder.For how to use the JMP 18 tool in our research, you can check in the JMP 18 folder.For all figures in the paper, you can check in the Figures folder.

Replication Package IntroductionThis is a replication package for my paper “Emerging Trends Software in Architecture Through the Practitioner Lens: A Five-Year Review”.Use/Check the replication packageRead the README.md to check the details.Preparation and installationFollow the instructions in INSTALL.md to install and configure all the tools.Using scriptCheck requirements.txt and download all the needed libs, then you can use the script directly.Results analysis for the RQsFor the raw data and search strategy, you can check in the Raw Data folder.For the LLM implementation process, you can check in the LLM Processing folder.For all scripts, you can check in the Scripts folder.For how to use the Gephi tool in our research, you can check in the Gephi folder.For how to use the JMP 18 tool in our research, you can check in the JMP 18 folder.For all figures in the paper, you can check in the Figures folder.

Replication Package IntroductionThis is a replication package for my paper “Emerging Trends Software in Architecture Through the Practitioner Lens: A Five-Year Review”.Use/Check the replication packageRead the README.md to check the details.Preparation and installationFollow the instructions in INSTALL.md to install and configure all the tools.Using scriptCheck requirements.txt and download all the needed libs, then you can use the script directly.Results analysis for the RQsFor the raw data and search strategy, you can check in the Raw Data folder.For the LLM implementation process, you can check in the LLM Processing folder.For all scripts, you can check in the Scripts folder.For how to use the Gephi tool in our research, you can check in the Gephi folder.For how to use the JMP 18 tool in our research, you can check in the JMP 18 folder.For all figures in the paper, you can check in the Figures folder.

LO2 datasetThis is the data repository for the LO2 dataset.Here is an overview of the contents.lo2-data.zipThis is the main dataset. This is the completely unedited output of our data collection process. Note that the uncompressed size is around 540 GB. For more information, see the paper and the data-appendix in this repository.lo2-sample.zipThis is a sample that contains the data used for preliminary analysis. It contains only service logs and the most relevant metrics for the first 100 runs. Furthermore, the metrics are combined on a run level to a single csv to make them easier to utilize. data-appendix.pdfThis document contains further details and stats about the full dataset. These include file size distributions, empty file analysis, log type analysis and the appearance of an unknown file.lo2-scripts.zipVarious scripts for processing the data to create the sample, to conduct the preliminary analysis and to create the statistics seen in the data-appendix.csv_generator.py, csv_merge*.py: These scripts create and combine the metrics into csv files. They need to be run in order. Merging runs to global is very memory intensive.findempty.py: Finds empty files in the folders. As some are expected to be empty, it also counts the unexpected ones. Used in data-appendix.loglead_lo2.py: Script for the preliminary analysis of the logs for error detection. Requires LogLead version 1.2.1.logstats.py: Counts log lines and their type. Used for creating the figure of number of lines per type and service.node_exporter_metrics.txt: Metric descriptions exported from Prometheus (text file).pca.py: The Principal Component Analysis script used for preliminary analysis.reduce_logs.py: Very important for fair analysis as in the beginning of the files there are some initialization rows that leak information regarding correctness.requirements.txt: Required Python libraries to run the scripts.sizedist.py: Creating distributions of file sizes per filename for the data-appendix.Version v2: Fixed LogLead version number and minor changes in scripts

LO2 datasetThis is the data repository for the LO2 dataset.Here is an overview of the contents.lo2-data.zipThis is the main dataset. This is the completely unedited output of our data collection process. Note that the uncompressed size is around 540 GB. For more information, see the paper and the data-appendix in this repository.lo2-sample.zipThis is a sample that contains the data used for preliminary analysis. It contains only service logs and the most relevant metrics for the first 100 runs. Furthermore, the metrics are combined on a run level to a single csv to make them easier to utilize. data-appendix.pdfThis document contains further details and stats about the full dataset. These include file size distributions, empty file analysis, log type analysis and the appearance of an unknown file.lo2-scripts.zipVarious scripts for processing the data to create the sample, to conduct the preliminary analysis and to create the statistics seen in the data-appendix.csv_generator.py, csv_merge*.py: These scripts create and combine the metrics into csv files. They need to be run in order. Merging runs to global is very memory intensive.findempty.py: Finds empty files in the folders. As some are expected to be empty, it also counts the unexpected ones. Used in data-appendix.loglead_lo2.py: Script for the preliminary analysis of the logs for error detection. Requires LogLead version 1.2.0.logstats.py: Counts log lines and their type. Used for creating the figure of number of lines per type and service.node_exporter_metrics.txt: Metric descriptions exported from Prometheus (text file).pca.py: The Principal Component Analysis script used for preliminary analysis.reduce_logs.py: Very important for fair analysis as in the beginning of the files there are some initialization rows that leak information regarding correctness.requirements.txt: Required Python libraries to run the scripts.sizedist.py: Creating distributions of file sizes per filename for the data-appendix.