Understanding the quality and evolution of Android app build systems
Bibliographic record
Abstract
Abstract Build systems are used to transform static source code into executable software. They play a crucial role in modern software development and maintenance. As such, much research effort has been invested in understanding the quality and evolution of build systems, including Apache ANT, Apache Maven, and Make‐based ones. However, the quality and evolution of build systems for mobile apps, such as on the Android platform, have not as yet been investigated in detail. Mobile app development, and the Android development context in particular, impose unique constrains, such as different device conditions and capabilities. It presents unique challenges, such as frequently upgraded Android frameworks, which those who implement and maintain build systems must tackle. In this paper, we present an exploratory empirical study of the build systems of 5222 Android projects to better understand their quality and evolution. We (a) study the build technology choices that Android developers make (Gradle being recommended and the most popular choice), (b) explore the sustainability of the official Gradle build system (parts of build files are updated more frequent that others and the update of the special Gradle plugin would induce unrecommended configurations), and (c) analyze the quality of Gradle scripts for Android apps—more than a half of the open‐source Android apps cannot be successfully built due to five common root causes.
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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 | 0.008 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".