Pure Data (PD) is a visual programming language for computer music that allows users to create applications through a graph-based, drag-and-drop interface, using objects and connections to manage program flow. There is a lack of tool support for computer musicians using PD, particularly for code completion. In this paper, we introduce TriGraph, a graph-based probabilistic model specifically designed for code completion in PD. TriGraph uses statistical analysis of 2-node and 3-node subgraph frequencies to predict nodes and connections in PD graphs. Using a dataset of parsed PD files, we train and evaluate 5 TriGraph models, assessing their performance in predicting nodes and edges in PD graphs. Our evaluations indicate that the models achieve an average Mean Reciprocal Rank (MRR) score of 0.39 for node prediction, placing the correct answer within the top 3 suggestions, and outperforming the n-grambased KenLM model on similar tasks. For edge prediction, the models achieve an average MRR score of 0.57, with results showing that incorporating both 2 -node and 3-node subgraphs yields better results than using only 3 -node subgraphs. These findings suggest that TriGraph could enhance the productivity of PD programmers by providing code completion support that may speed up development, reduce errors, and assist in discovering available options. These potential benefits highlight its promise as a valuable support tool for end-user programmers in graphical environments.

Pure Data (PD), a data-flow based visual programming language utilized for music and sound synthesis, remains underexplored in software engineering research. Existing literature fails to address the nuanced programming practices within PD, prompting the need to investigate how end-users manipulate nodes and edges in this visual language. This paper systematically extracts and analyzes 6,534 publicly available PD projects from GitHub. Employing source code parsing, pattern matching, and statistical analysis, we unveil usage patterns of PD by the end-user programmers. We found that most revisions of the PD files are small and simple, with fewer than 64 nodes, 51 connections, and 3 revisions. Most PD projects have less than 17 PD files, 31 commits, and only 1 author working on the PD files. The median differences in the number of nodes and edges between each commit and its parents, modifying the same file, are 3 and 0, respectively, implying small changes across various revisions of a PD file. Our findings contribute a valuable dataset for future studies, addressing the dearth of research in PD. By unraveling usage patterns, we provide insights that empower scholars and practitioners to optimize the programming experience for end-users in the realm of visual programming languages.

Software testing helps developers minimize bugs and errors in their code, improving the overall software quality. In 2013, Kochhar et al. analyzed 20,817 software projects in order to study how prevalent the practice of software testing is in open-source projects. They found that projects with more lines of code (LOC) and projects with more developers tend to have more test cases. Additionally, they found a weak positive correlation between the number of test cases and the number of bugs. Since the conclusions of a study might become irrelevant over time because of the latest practices in the relevant fields, in this paper, we investigate if these conclusions remain valid if we re-evaluate Kochhar et al.’s findings on the Java projects that were developed from 2012 to 2021. For evaluation, we use a random sample of 20,000 open-source Java projects each year. Our results show that Kochhar et al.’s conclusions regarding the projects with test cases having more LOC, the weak positive correlation between the number of test cases and authors, and the weak positive correlation between the number of test cases and bugs remain stable until 2021. Our study corroborates Kochhar et al.’s conclusions and helps developers refocus in light of the latest findings regarding the practice of software testing.