Bibliographic record
Abstract
Software developers heavily rely on third-party libraries to accomplish their programming tasks. Since many libraries offer similar functionality, it can be difficult and tedious for developers differentiate similar libraries in order to select the most suitable one. In our previous work, we proposed the idea of metric-based library comparisons that allow developers to compare various aspects of libraries within the same domain, empowering them with information to aid with their decision. In this paper we present an IntelliJ plugin, LibComp, that provides this library metric-based comparison technique right within the developer’s IDE. As soon as a developer adds a library dependency that LibComp has information about, LibComp will highlight this dependency to let the developer know that there are alternatives available. Once the user triggers the comparison for that library, they can view various metrics about the library and its alternatives and decide if they want to use one of the alternatives. In the process, LibComp also records the number of times the developer invokes the tool and any completed replacements. Such feedback, if optionally sent to us by the developer, provides us valuable insights into developers’replacement decisions as well as information on how we can improve the tool. A video demonstrating the usage of LibComp can be found at https://youtu.be/YtEEdJan77A
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".