Exploring the Adoption of Fuzz Testing in Open-Source Software: A Case Study of the Go Community
Bibliographic record
Abstract
Fuzz testing (or fuzzing) is a software testing technique aimed at identifying software vulnerabilities. Recently, the Go community added native support for fuzz testing into their standard library. Using that feature, developers can write unit tests to perform deterministic and fuzz testing of their software systems against unexpected inputs. Although the availability of support makes fuzz testing more accessible for the Go community at large, little is known about the degree to which Go developers adopt fuzz testing during software development. Therefore, in this paper, we set out to study the evolution of fuzz testing practices in open-source Go projects. More specifically, we strive to understand whether the introduction of support for fuzz testing in the Go standard library has led to the adoption of fuzz testing as part of the standard testing processes of Go projects. To achieve our goal, we study 1) to what extent fuzz tests are used in open-source Go projects, 2) who writes and maintains fuzz tests in Go projects, and finally, 3) how tightly coupled are fuzz tests with source code (as compared to non-fuzz tests). We find that fuzz testing only represents 3.15% of testing functions in open-source projects. Our results also suggest that fuzz testing development is not being conducted as part of standard testing activities. For developers contributing to fuzzing, we find that a median of only 12.50% of their testing-related commits contain fuzz tests. Finally, we perform a qualitative analysis and find that fuzz testing is mostly used by critical software systems, such as blockchain technologies or network infrastructure projects, to test the most critical features of their systems (e.g., data processing functions, database endpoints). Our results lead us to conclude that fuzz testing is best used in combination with deterministic testing (e.g., unit testing) where fuzzing is used to thoroughly test important features, and deterministic testing is used to test other features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".