Understanding the use of lambda expressions in Java
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Bibliographic record
Abstract
Java 8 retrofitted lambda expressions, a core feature of functional programming, into a mainstream object-oriented language with an imperative paradigm. However, we do not know how Java developers have adapted to the functional style of thinking, and more importantly, what are the reasons motivating Java developers to adopt functional programming. Without such knowledge, researchers miss opportunities to improve the state of the art, tool builders use unrealistic assumptions, language designers fail to improve upon their designs, and developers are unable to explore efficient and effective use of lambdas. We present the first large-scale, quantitative and qualitative empirical study to shed light on how imperative programmers use lambda expressions as a gateway into functional thinking. Particularly, we statically scrutinize the source code of 241 open-source projects with 19,770 contributors, to study the characteristics of 100,540 lambda expressions. Moreover, we investigate the historical trends and adoption rates of lambdas in the studied projects. To get a complementary perspective, we seek the underlying reasons on why developers introduce lambda expressions, by surveying 97 developers who are introducing lambdas in their projects, using the firehouse interview method. Among others, our findings revealed an increasing trend in the adoption of lambdas in Java: in 2016, the ratio of lambdas introduced per added line of code increased by 54% compared to 2015. Lambdas were used for various reasons, including but not limited to (i) making existing code more succinct and readable, (ii) avoiding code duplication, and (iii) simulating lazy evaluation of functions. Interestingly, we found out that developers are using Java's built-in functional interfaces inefficiently, i.e., they prefer to use general functional interfaces over the specialized ones, overlooking the performance overheads that might be imposed. Furthermore, developers are not adopting techniques from functional programming, e.g., currying. Finally, we present the implications of our findings for researchers, tool builders, language designers, and developers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it