Rust is a general-purpose programming language that has an emphasis on performance while also being type-, memory-, and thread-safe (Rust). To maximize efficiency, Rust does not use garbage collection (GC). Instead, it imposes a compiler-enforced discipline of ownership and lifetimes (based on linear logic (Wadler90:Linear) and region-based memory management (tofte97regions; GrossmanMJHWC02), respectively) to ensure that no reference will be used after its referent is freed. This imposes restrictions on aliasing: an alias can be only be borrowed temporarily, and doing so limits mutation (which also helps avoid data races). For example:
Here, function foo defines a String owned by variable s1. It then calls calc_len to compute its length by lending a reference &s1 to the called function, which may only read it, not write it. When the function returns, the borrowed reference’s lifetime ends so it is dropped, which restores full ownership to s1. When function foo completes, s1’s lifetime ends so it is dropped and the data is freed.
1.1. Rust is Hard to Learn and Use
Despite its performance and safety advantages and a loyal core of devotees (rustloved), Rust remains relatively unpopular; the TiOBE index ranks Rust at #27 as of July 2021 (TiOBE), and the IEEE Spectrum ranks Rust at #20 (Spectrum:Rank). In the interest of bringing the benefits of the design ideas behind Rust to more software projects, it is worth considering why Rust has seen limited adoption.
A partial explanation resides in the difficulty of learning Rust. Fulton et al. (Fulton2021:Benefits) interviewed and surveyed software practitioners who adopted or attempted to adopt Rust, finding that 59% of survey respondents felt that Rust was harder to learn than other languages. Seven of the 16 interviewees reported that the biggest challenges in learning Rust were the borrow checker—the part of the compiler that enforces the ownership/borrowing discipline—and the overall change of programming paradigm to one that requires that the compiler be able to reason about lifetimes. Ashley Williams, the interim executive director of the Rust Foundation, agreed: “[References and borrowing] is notoriously something that people find to be the most difficult part of learning Rust” (Williams21). The difficulty of learning Rust has implications on adoption in software teams: 42% of respondents in Fulton et al.’s survey were concerned about their ability to hire Rust developers, since it would take a long time for new team members to become productive.
In addition to being challenging to learn, Rust can be difficult to use. Programming with DAGs and cyclic data structures is straightforward in most popular languages, but such structures do not conform to Rust’s aliasing restrictions. Rust provides safe building blocks to work around these restrictions. The structure Rc<T> is for references to immutable T objects with manually managed reference counts (confirmed safe by the borrow checker), and RefCell<T> is a container for a mutable T object with dynamically tracked borrowing. Together (e.g., Rc<RefCell<T>>) these can implement a discipline of interior mutability that supports rich aliasing patterns (interior-mut), but in a manner far more complex than most programmers are used to. As such, they may be tempted to use Rust’s unsafe feature to sacrifice safety for ease of use.
Easing Use with Garbage Collection?
Rust’s restrictions are due to the compiler’s inability to verify the safe use of richer, aliased data structures. These restrictions would not be needed if Rust used GC. As such, an extension to Rust that included GC could enable programmers to be productive sooner, without having to learn the trickier parts of the language right away. By making the GC optional, programmers could still learn and use the harder parts of Rust later, and convert their GC-using code to traditional Rust as needed to improve performance.
Moreover, by making a Rust GC library-based, it could even prove useful to experts. While 63% of the respondents in the survey by Fulton et al. (Fulton2021:Benefits)
cited lack of GC as a reason to use Rust, 87% cited high performance as a reason—between the two is a category of user open to the idea that high performance and garbage collection are not always at odds. Most code is not performance-critical: a guideline is that 90% of the time is spent executing only 10% of the code(Aho1992:Foundations). Thus GC- and manual-based memory management could coexist. Experiments in Cyclone (GrossmanMJHWC02), a C-like systems programming language, found nearly no performance cost of using GC when it was applied judiciously alongside safe, manual techniques (swamy05experience).
1.2. Bronze: A Library-based GC for Rust
This paper presents Bronze,111So named for bronze’s corrosion resistance. a new Rust library that provides a clean garbage collection interface, and an experiment evaluating the possible benefits of using Bronze while learning Rust. By creating a library in which GC is optional, we were able to study the effects of GC without including the rest of the language design as an independent variable in the study.
Bronze provides a structure, GcRef<T>, that implements a garbage-collected reference to a mutable object; such references are not subject to Rust’s aliasing restrictions. Unlike other Rust GC designs, Bronze uses LLVM stack maps to automatically find roots, obviating the need for programmers to specify tracing roots manually.
We deployed Bronze in an IRB-approved, randomized, controlled experiment in a sophomore-level programming languages course. Because the course was required for graduation at a university with a large computer science enrollment, we were able to recruit from a population of 633 students who were enrolled in the course. All students carried out a multi-part Rust programming assignment, but those who agreed to participate in the research were randomly assigned to condition Bronze or Traditional; the former group used Bronze in the assignment while the latter group did not. Ultimately, 333 students were part of the random assignment, and 428 students participated in the study in some way.
The experiment design is shown in Table 1; the tasks are described in detail in section 3.1. In the assignment, students were given functions and declarations that needed to be completed; Bronze participants were given versions of the interfaces that were structurally similar but which had been adapted to use GC. The assignment parts were cumulative, and the final step for Bronze participants was to redo their previous implementation without using GC, ensuring all participants learned how to use traditional Rust. We provided students with unit tests (including source code), and we assigned grades according to which of the unit tests passed.
|Topic||Traditional task||Bronze task|
|Aliased, mutable data||Aliasing||Aliasing|
|Aliased, mutable data||(none)||Aliasing|
At the conclusion of the study we carried out both quantitative and qualitative analysis of measured data (e.g., project score) and survey responses. Because of our large sample, we were able to reserve 10% of the participants’ data (with their survey responses) for exploratory analysis. This allowed us to explore which hypotheses might be worth testing while preserving soundness, since the results in section 5 are based on the remaining 90% of the data.
We did not observe a difference between conditions in completion rates (fraction of students scoring 100%) or in times completing Ownership. However, students who used Bronze were more likely to complete their aliasing task (Aliasing) than Traditional students were to complete theirs (Aliasing), and did so significantly faster, spending a median of 4 hours instead of 12 hours. The additional time spent by Bronze students doing the aliasing task again, this time without GC (Aliasing) resulted in no significant difference in total time spent on the project. We conclude that for newcomers to Rust, if the goal is simply to accomplish a programming task, garbage collection may present a significant benefit for productivity. Further, there may be enough advantage to using garbage collection while learning Rust to compensate for the additional time required to learn, apply, and switch to traditional Rust memory management approaches.
Survey results confirmed past reports (Williams21; Fulton2021:Benefits) that ownership and borrowing are significant programming challenges. Participants were much more likely to believe that GC makes writing programs easier after completing the experiment than they were before the experiment. Bronze participants were more likely to believe that GC made writing programs easier after completing the whole assignment than they did at the beginning, and 64 of the 89 Bronze participants (72%) who responded to the question strongly agreed at the end that using GC makes writing programs easier. Only 3 (3%) strongly disagreed. Interestingly, although participants reported that GC made programming easier, participants who used GC did not report liking Rust significantly more than participants who did not. We observed a stronger negative correlation between liking Rust and stress than between liking Rust and time spent relative to participants’ expectations.
From these results, we derive recommendations for software engineers and language designers. Software engineers should be aware of a possible productivity benefit of garbage collectors relative to using Rust’s aliasing restrictions; it may be better to re-architect a system, use a garbage collection library, or use a garbage-collected language if the architecture cannot be changed. If engineers use a library-based GC with Rust and need to remove it to improve performance, using GC saves enough time that programmers can switch away from GC without a significant loss in productivity.
That positive feelings about Rust were more strongly correlated with frustration and stress than with time spent on the assignment suggests that language designers who want to promote adoption (by making languages programmers like) should consider focusing on how to reduce stress, such as by making progress more predictable, rather than how to (only) maximize programmer productivity.
In survey responses, participants reported extreme difficulty with references, lifetimes, and ownership. Participants said examples and live coding demonstrations helped them learn these concepts most effectively. These responses have implications for pedagogy: we hypothesize that using examples and live coding demonstrations is more effective to explain these challenging Rust concepts than traditional slide-based explanations.
Key contributions of this paper include:
The design and prototype implementation of Bronze, a GC for Rust that is simpler to use than prior Rust GCs.
A randomized controlled trial of Bronze, in which we found that Bronze can enable more people to complete tasks within time limits and, among those who finished, significantly reduce time required. We also collected qualitative data, confirming that ownership, lifetimes, and references are particularly challenging for new Rust programmers.
2. Bronze: Design and Implementation
Bronze introduces GcRef<T>, which represents a reference to a value of type T that exists in a garbage-collected portion of the heap. GcRef<T> implements the Deref trait, so the * operator can be used to obtain a reference to the underlying value. If one has a mutable GcRef<T>, the reference can be used to mutate the value. GcRef::new(v) moves value v into the garbage-collected portion of the heap and generates a GcRef pointing to it.
Rust permits only one mutable reference to a value at a time. For greater flexibility, (standard) Rust supports interior mutation of an object through an immutable reference to it (interior-mut). The programmer may borrow a special reference to the value that permits mutation, and the runtime ensures that only one such reference can exist at a time, enabling a safe relaxation of compile-time checks. With Bronze, mutation is permitted through all references to each garbage-collected object, with no extra effort. A key tradeoff is that Bronze does not guarantee thread safety; as in other garbage-collected languages, it is the programmer’s responsibility to ensure safety. For example, Figure 1 shows how GcRef simplifies code when there are multiple mutable aliases.
Bronze is a precise, mark/sweep garbage collector. We selected this design rather than using a conservative collector (boehm97gc) because precise collection has the potential for better performance and complete collection of garbage. As our primary objective in the design was usability, we designed Bronze to find roots automatically. Most garbage collectors for Rust include root and unroot methods that must be manually called by the user of the GC library (rust-gc; Josephine; Shifgrethor).
Bronze defines the Trace trait, which indicates functionality used by the garbage collector to trace through the object graph to find live objects. Only types that implement Trace can be put on the garbage-collected heap. Bronze also defines the Finalize trait, which allows users to write code that runs just before an object is deallocated by the collector; it serves as an alternative to drop, which is for deallocation that runs at a statically-determined time. Bronze provides macros that automatically derive implementations of Trace and Finalize for straightforward types, but users can provide their own implementations if needed.
Rust’s compiler translates Rust code to LLVM IR, which has a primitive that allows emitting stack maps to annotate the stack with compiler-specified metadata. In the case of Bronze, the metadata allow the runtime to determine which stack addresses correspond with objects that must be traced because they may reference garbage collected objects. The Bronze tracer relies on a modified version of the Rust compiler that emits stack map information in the emitted LLVM IR. The particular stack map mechanism used by Bronze assumes that the program is single-threaded; other available mechanisms could relax this requirement in the future at additional engineering cost for Bronze.
Bronze uses a mark/sweep algorithm based on Goregaokar’s implementation (rust-gc): To identify which objects can be collected, the runtime keeps a linked list of all objects that it allocated. Then it can collect objects on the list that were not marked by the tracer. Bronze’s mark/sweep implementation is only a proof of concept, however, and is not production-ready. In particular, the implementation can trace local variables of type GcRef<T>, but additional work is required to enable tracing of arbitrary types. In the experiment, we used a version of Bronze that never collects. This approach was suitable for the small-scale programs that were used, since they do not allocate enough memory to require collection. We think it is unlikely that a full, performant implementation would require changes to the design, since the work that remains should be beneath the interface.
Bronze is available on Cargo, the Rust package manager.222https://crates.io/crates/bronze_gc
We conducted an experiment in which we randomly assigned participants to use either traditional Rust or Bronze when completing a multi-part programming assignment. We measured their performance and surveyed them about their experiences. The study was approved by our IRB.
We devised a multi-part programming assignment for our study. The specification of each part differed slightly depending on whether participants should use GC or traditional Rust; we label a task with subscript GC or noGC to clarify this, as needed.
The Basics task, carried out using traditional Rust, focused on the basics of Rust syntax. The Ownership task introduced ownership and borrowing. This and later parts involve programming a simulation of turtles living on the university campus. The instructions333Instructions have been slightly edited for space. included:
Completing this part required understanding ownership transfer (add\_turtle), references (get\_turtle), iterators (turtles), options and borrowing (fastest\_walker), and mutable vectors (add\_turtle and breed\_turtles). In Ownership, the get\_turtle method of Campus returned a value of type TurtleRef<’a>, which was defined as \&’a Turtle. In Ownership, TurtleRef was defined as GcRef<Turtle>.
The Aliasing task introduced the requirement of multiple mutable references. The instructions included:
Campus needed a vector of turtles, each of which needed a vector that had references to the same turtles referenced by Campus. Because of breed\_turtle, the turtles needed to be mutable. Finally, the cache required returning collections that referenced the same turtles.
For Aliasing, these requirements could be addressed using reference counting and interior mutability. The get\_turtle method of Campus, as in Ownership returned a value of type TurtleRef, but TurtleRef was now a struct with fields that the participants needed to define. TurtleRef, in turn, exposed a method borrow\_turtle, which returned a value of type BorrowedTurtle. BorrowedTurtle implemented the Deref and DerefMut traits, allowing clients to obtain a temporary mutable reference. Participants were required to fill in the fields of TurtleRef and BorrowedTurtle; a typical solution was Rc<RefCell<Turtle>> and RefMut<’a, Turtle>. This approach allowed external clients to obtain references to Turtles that could be made temporarily mutable if needed for the application.
In Aliasing, TurtleRef is defined as GcRef<Turtle>, just as in Ownership—because GcRef supports mutation of the referenced value, there is no need for additional structure.
Bronze participants were asked to complete task Aliasing after completing Aliasing.
We recruited participants from the 633 students who were enrolled in a required, sophomore-level programming course. Our programming assignment was part of the course’s grade, but participation in the research was voluntary, and confirmed by informed consent. Research participants were randomly assigned to use either Bronze or traditional Rust, and agreed to take a survey after completing each part of the assignment. In doing so, they received extra credit on the assignment—1% extra credit per survey, or 5% for all three. Participants were free to withdraw from the experiment at any time; students who started the experiment but withdrew received 2.5% extra credit. Students who withdrew, or opted not to participate at all, could complete any version of the assignment. Students had the option of accepting random assignment and/or carrying out surveys but not having their data included in our research analysis; we awarded extra credit independently of this choice. Only three students did not consent to their data being used.444Students were required to opt out if they were under 18 years old.
The instructor gave lectures on programming in Rust during four 80-minute class periods over a period of two weeks (April 13–27). The Basics task in the assignment overlapped with, and was due at the end of, the second week of lectures (April 21–29). The remaining parts of the assignment were released on April 29 and were due May 11.
The README file for the Ownership and Aliasing tasks described the study in detail and linked to a Qualtrics survey, which included a consent form and requested demographic information from students who consented. The form also asked for their university ID number so that we could associate student grades with participants. The Qualtrics tool randomly assigned participants to a condition (Bronze or Traditional) upon consent, emailing them which condition they were assigned to. The email also contained a personalized link to a survey to fill out after completing each part of the programming assignment, allowing us to track which students had completed the surveys.
The course used a question-and-answer web site, Piazza (Piazza), to allow the students to ask questions about course content. Because we had revised the Rust content relative to the prior semester, the first author joined the teaching assistants (TAs) in answering questions about the assignment. We made sure to answer all questions in a timely fashion with high quality. In addition, as a result of posts on Piazza indicating that students found the assignment difficult, the first author conducted live-coding demonstrations on May 6 and 7.555Student privacy regulations preclude us from sharing the videos of these demos. Demonstrations delved into topics covered in class, including Rc, RefCell, smart pointers, mutability, scope and borrowing, a comparison between GC and reference counting, string literals, interior mutability, mutable structs, lifetime specifiers, and Box.
Students’ abilities to complete the assignment depended to some extent on the quality of our instruction and the extent to which we emphasized each topic. To help decouple our instructional design from the experiment, we based our instructional materials on the online Rust book (Klabnik2018:Rust) and leveraged materials that had been used successfully in previous editions of the course.
Because the first author taught the live coding demonstrations and answered many student questions on Piazza, it is possible that this could have introduced bias. One of two course instructors is also a co-author, so there could have been bias in teaching as well, since GC and interior mutability, which were taught in the course, were of interest in the study.
Our study focused on programmers who are new to Rust; our sample was of students. While professionals (many of whom are also new at Rust) would have more programming experience, prior work (Naiakshina2020:Conducting; Naiakshina2019:If; Acar2017:Security) suggests that results with students corresponds with results with professionals in related, but not identical, tasks.
All times spent by participants were self-reported. Although we asked participants to use a tool to track time spent for Ownership and Aliasing, we did not confirm that they did so. However, any noise or bias in reported times are likely to be consistent across experimental conditions. Although we asked participants to complete the surveys immediately after completing each part of the assignment, some participants submitted the surveys in batches.
The extra credit incentive could have resulted in a non-uniformly selected sample. However, as we discuss in more detail in section 7, the small difference in median class grade between participants and non-participants (90% vs. 87%) suggests that this does not result in a significant threat to validity.
More Bronze participants withdrew from the study than Traditional participants (see section 7); the withdrawing Bronze participants may have been weaker, less industrious, or more risk-averse than the students that remained, leaving a stronger overall Bronze population compared to the traditional one. We believe the magnitude of the difference in times between conditions (section 5.4) is large enough that it cannot be explained by this possibility.
All of the students worked on the assignment in the same timeframe, so trends over time could have been due to external influences, such as stress caused by the approach of final exams. However, these trends would have been equally applicable across experimental conditions.
In this section, we describe the analysis we conducted.
Of 633 students who were enrolled in the course, 385 students signed up for the experiment, with 190 assigned to use Bronze and 195 to Traditional. Of these, 41 withdrew; an additional 11 students submitted code for both versions of the assignment. We did not analyze the data from those 52 students, leaving experimental data from 333 students for analysis: 139 Bronze, and 194 Traditional.
383 students completed 1120 surveys. 34 students submitted surveys but did not accept random assignment. 36 accepted random assignment but did not submit surveys. Overall, 428 students participated in the study. Only the data from the 333 experiment participants were used in the analyses below, except for the overall demographics of the population and the analysis of whether GC participants thought GC makes programming easier (section 5.5). For each task, we analyzed the corresponding survey data, as shown in Table 2.
|Random assignment to condition||194||139|
We asked students about their Rust experience. Of the 333 experiment participants, 84 had read about Rust or talked to a friend about it. 12 had played with Rust on their own, two had used it on a team, and three had used it in an open-source project. When asked to self-assess their prior level of Rust knowledge, 307 reported no experience; 17 reported passing familiarity (maybe wrote a few lines of code); 6 reported a moderate amount or a lot of experience.
shows the distribution of course grades by decision to enroll in the experiment in superimposed violin and box plots. To test whether the decision to enroll was related to course grade, we conducted a logistic regression. We found, with higher grades being associated with choosing to enroll in the study. The odds ratio was
with a 95% confidence interval of, meaning that a 1-point increase in grade correlates with multiplying the odds of enrolling by .
5.2. Analysis Methods
We designed the overall experiment to allow us to examine the relationship between time spent and outcomes (grades and completion); the association between testing practices, sources of help, and editor selection and outcomes; and the effect of condition on the amount of time and fun students predicted if they had done the assignment in other languages, as well as the effect of condition on amount and sources of help.
In order to help us refine our list of hypotheses while ensuring a sound data analysis, we randomly reserved data from 10% of the participants for exploratory analysis. We visualized this exploratory data and conducted statistical tests to identify interesting hypotheses about the effects of garbage collection and other factors that might influence outcomes. From this exploration, we selected specific questions of interest and associated hypothesis tests. Finally, we discarded the reserved 10% and performed the planned tests on the remaining 90%. All results reported below are drawn from this 90%, except overall demographic information (e.g., participation rates), which is reported for the entire dataset.
Because we conducted multiple hypothesis tests, we interpret the results with a Holm-Bonferroni correction (Holm1979:Simple). All of the reported p-values are corrected and can be compared directly with ; we set in our interpretation.
5.3. Completion rates
We wanted to know whether Bronze participants were more likely to finish either individual parts of the assignment or the assignment as a whole. We conducted Fisher’s exact tests to compare completion rates (rates of scoring 100%) on each part of the assignment across conditions. Completion rates of each part are shown in Figure 3. For Ownership, we found no significant difference in completion rate (). Comparing Aliasing (Traditional) and Aliasing, we found that Bronze users were significantly more likely to score 100% (). The odds ratio was approximately , indicating that the odds of finishing for participants not using Bronze were approximately
times the odds of finishing for participants who DID use Bronze. When considering probability of completing all parts, Fisher’s exact test indicated no significant difference between conditions ().
5.4. Completion times
We hypothesized that Bronze would enable participants to complete tasks faster. We analyzed the distribution of completion times of tasks after Basics. A Shapiro-Welk normality test found a likely violation of normality of the completion times (). Therefore, we conducted nonparametric Wilcoxon tests rather than ANOVA tests. Figure 4 shows the total times reported across the conditions.
We did not find a significant difference in Ownership time (). However, Bronze participants finished Aliasing significantly faster (median = 4 hours) than Traditional Rust participants (median = 12 hours) (). We did not find a significant difference in total completion time ().
We also compared completion times spent by participants who did not finish. A Wilcoxon rank sum test found no significant difference (). We also compared times reported by participants who scored 100% to times by participants who did not, finding no significant difference ().
Figure 4 also shows the distribution of times for Aliasing when completed by Bronze participants. The median time was 8 hours (mean = 9.9, ). The difference between medians of Aliasing times across conditions was also 8 hours, explaining why we did not find a difference in total time spent across conditions.
5.5. Does GC make writing programs easier?
If users feel that GC makes programming easier, then including GC in a language might improve language adoption rates. We were interested in how Bronze participants’ opinions of GC changed while using it. Responses to our question about whether GC makes programs easier were on a Likert scale with a 0 to 4 point range, with 4 indicating a strong belief that GC made programming easier. Median scores were 2 after Basics and 4 after Aliasing. Thus, after trying the assignment with and without GC, they recognized a strong benefit to GC. An ordinal regression yielded (odds ratio 21.4), indicating a significant effect of doing the assignment on beliefs about GC helpfulness. Figure 5 illustrates how participants’ beliefs about GC changed over time.
5.6. Liking Rust: comparison between conditions and over time
We hypothesized that if Bronze helped participants complete tasks faster, Bronze participants might like Rust better than Traditional participants. Each survey asked: “How much do you like Rust?” on a four-point Likert scale (we omitted a neutral option in order to force expressing a preference). Figure 6 shows the responses. We conducted an ordinal regression to assess whether there was a difference in Likert-scale responses across conditions after experiment participants were finished with the project (including those who did not score 100%). We found , indicating no significant effect of GC on liking Rust by the end of the assignment. We also compared responses to this question after Ownership; the ordinal regression gave , also indicating no significant difference.
5.7. Estimated time and fun in other languages
We were interested in how usage of Bronze might impact beliefs about time required in other
languages, since a high estimate of time in another language might correlate with a higher chance of choosing Rust, and since a good experience with GC might lead to higher estimates in languages that do not provide GC. In each survey, we asked participants: “Suppose you had done this part of the assignment in a different language instead. How much time would it have taken in THAT language compared to using Rust?” We asked a corresponding question asking about prediction of fun compared to Rust. Responses were on a five-point Likert scale, with an additional “I’m not familiar enough with this language to judge” option. We asked about C, C++, Java, and Python. Our exploratory data analysis led us to hypothesize that the relationships were weak, if they could be found at all. The strongest relationship appeared to be with prediction of time in C, and we hypothesized that Bronze participants would estimate longer times in C than the non-Bronze participants, since they would be more aware of the cost of manual memory management.
To compare predicted time in C across the two conditions (for which we received 864 responses from people familiar enough with C to answer), we used an ordinal regression mixed model. This approach accounted for the fact that we asked the same question of each participant after completing each part of the assignment. We found no significant difference ().
5.8. Correlations among opinions
To improve adoption of a language, it might be useful to understand what factors of a programmer’s experience correlate with liking a language. After participants completed each part of the assignment, we asked them how much stress, time, intellectual challenge, and frustration they experienced compared to their expectation, as well as how much they liked Rust. We were interested the extent to which feelings about Rust were correlated with those aspects of their experience, and how much those aspects correlated with each other. Figure 7 shows the pairwise correlations. Stress, time, intellectual challenge, and frustration appear to be strongly correlated, and assessments of whether participants like Rust are moderately correlated with stress and frustration.
5.9. Amount learned about Rust
After completing each part of the assignment, we asked participants how much they felt they learned about various topics in Rust. Figure 8 shows the results for ownership and borrowing. The amount participants reported learning in each part of the assignment does not appear to depend on whether participants used Bronze.
5.10. Factors influencing grades
Factors other than usage of Bronze may affect success; we hoped to understand what factors were relevant. In our exploratory data analysis (on 10% of the data), we used a linear mixed-effects model to evaluate the relationship between methods that participants reported using and their assignment grades. We looked at time spent, sources of help, and choice of developing on their own machine or on a server. Based on this, we hypothesized that course grade and development location might be significant factors. Students who do the work on a department server may have less access to computing resources or less skill at configuring systems. A linear regression found a significant positive correlation with course grade (, adjusted ) and that a 1-point increase in course grade corresponded with a -point increase in assignment grade. Development location was not a significant factor.
Because course grade only explained a small amount of the variance in assignment grade, we did additional exploration with the 10% withheld samples to identify a stronger predictor of grades, focusing on the whole assignment grade (rather than individual tasks). Returning to the remaining dataset, when predicting score on parts afterBasics from hours spent and course grade, we found that a significant correlation only with grade (, adjusted ). However, when predicting grades for Aliasing only (both GC and non-GC, with grades re-scaled from 0 to 100%), we found that time spent and course grade were both significant predictors ( for both factors). The model for Aliasing explained 20.9% of the variance in grades ().
6. Qualitative Analysis of Responses
The surveys included two free-response questions: “What aspects of this part of the assignment did you find most difficult?” and “What should we change about the class to help future students understand the concepts in Rust better?” We conducted a thematic analysis (Braun2006:Using). A single domain expert inductively developed the codebook and coded all responses, in consultation with the research team. Because answers to the two questions overlapped, we analyzed all responses together. We were interested in gleaning insights about language design as well as pedagogy from the responses.
References, lifetimes, ownership among the most challenging aspects
We received 1,143 comments on challenges that participants faced. 340 pertained to references, of which 204 were about lifetimes. 116 comments indicated syntax had been challenging. 100 mentioned interior mutability, 83 dynamic borrowing, and 70 mutability.
Consistent with earlier reports (Williams21; Fulton2021:Benefits), 199 participants reported that ownership was challenging; 76 such reports were about borrowing. One student, after finishing Aliasing, reported: “Learning rust ownership is like navigating a maze where the walls are made of asbestos and frustration, and the maze has no exit, and every time you hit a dead end you get an aneurysm and die.” A Traditional student reported: “Coding with ownership rules and trying to implement mutability, in general, was just such a headache. It is like someone had combined the worst part of C and Java.”
Aliasing required designing structures that supported dynamic borrowing — a borrowed reference whose safety was checked dynamically rather than statically. This required understanding the Rust Ref and RefMut structures, which dynamically track outstanding borrows of cells. One participant explained the difficulty: “It took me a long time to figure out the field types with the correct lifetimes…Once I knew what the types were, it took me about an hour or two to figure out the rest of the functions. It was quite confusing and frustrating figuring out how to use Rc and RefCell in the right spots, and my initial approach did not use them nearly as much as my final solution did.”
Error messages can help, but may not aid design or comprehension
Although Rust’s error messages have a reputation for being high-quality (Fulton2021:Benefits) (one participant wrote “the Rust compiler pretty much just wrote the program for me”), the compiler cannot give high-level design feedback. One GC participant explained: “When I was testing my non-GC version, I’d never run into so many errors in my life. When I tried fixing my errors, new ones just came up. I’ve heard students compare the debugging process for the non-GC version to a never-ending game of whack-a-mole.” Another reported: “Translating from GC to no GC was a nightmare. It wasn’t as simple as switching GcRef<Turtle> to Rc<RefCell<Turtle>> because of the new structs. Figuring out the typing of the structs along with debugging all the mutable references errors that resulted took hours and even some of the TAs couldn’t help me.” Error messages could also lead to working code without teaching the programmer what was wrong: “Getting weird errors that I still don’t understand but just fixed by listening to the compiler.”
Error messages also tend to give local advice that does not necessarily lead the user in the right direction. One participant reported “error messages were cyclical with things like remove \& then after removing try adding \&.” Three participants said it was possible to fix errors by following instructions without understanding what was going on. A higher-level error management approach, and better integration into an IDE (e.g., with visualizations (Hill2002:Scalable)), could help.
Garbage collection avoids mutability problems
We received 103 comments regarding garbage collection. 28 asked us to explain GC more thoroughly. 43 said they had a hard time understanding GC. However, some Bronze participants observed, after finishing the assignment, that GC had been very helpful. One reported: “After doing this part, I actually realized the power of garbage collection. Using RefCell and Rcs to create interior mutability, etc. is so hectic. I would always prefer GcRef! Technically Rc<RefCell<T>> acts like GcRef but much more work!” Another participant said: “Understanding references, lifetimes, and mutability without garbage collection is very difficult. It is not intuitive or understandable without GC.”
Free copying of GC references appeared to be critical to the helpfulness of GC. One participant said: “The transition from garbage collection to non-gc was rough. I think the garbage collection was as easy as it was because it implemented the Copy trait. My most common error in this project was the one where a certain variable was moved because it was of a type that didn’t implement copy. If not for some TA help, I would have been completely lost.”
Students wanted more time and more examples to support learning
We received 715 comments regarding the course design, assignments, lectures, and recitations. Participants reported that the Aliasing task was extremely challenging, and that previous parts of the assignment left them unprepared for it. 182 comments asked us to revise the project design or clarify the specifications.
190 comments asked for a longer, more complete treatment of Rust in future versions of the course. 54 asked for an intermediate-level assignment. One participant put the steep learning curve (despite splitting the work over multiple parts) as follows: “I think having this …project is a bit steep. I felt like I was being thrown into a big steaming wok.” In particular, students requested more or more detailed examples (74), more or continued live coding demonstrations (35), and more discussion sections (31).
We discuss key takeaways and lessons learned from our experiment.
Even with GC, Rust learners need to understand ownership, borrowing, and lifetimes
When designing the experiment, we were concerned that using GC would allow participants to avoid learning about ownership, borrowing, and lifetimes. Because we were hoping Bronze would be an aid to learning traditional Rust, this might have been a problem. However, because of the fundamental way ownership is used in Rust, much of the code required understanding ownership and borrowing even with GC. In this assignment, GC primarily served to aid situations that involved multiple references to values. As Figure 8 shows, participants reported learning similar amounts about these critical topics across conditions.
Most of the benefit of GC comes from architectural simplification
Participants reported that the architectural requirements in Aliasing were extremely challenging; it is likely that the design was a significant contributor to the difference in performance between non-Bronze and Bronze participants. In particular, although Bronze participants only needed to return GC references, Traditional Rust participants participants needed to fill in TurtleRef and BorrowedTurtle structures, which required additional design insight. The challenge posed by this design was apparent in the survey responses, in which 100 comments pertained to interior mutability and 83 to dynamic borrowing (almost as many total as the 199 who complained about ownership or borrowing). We conclude that a significant part of the benefit of GC in Rust programs is the architectural simplifications it enables and promotes.
Participants and non-participants were comparable
We were surprised that students with higher grades were more likely to enroll; we had expected students with lower grades would be more incentivized by the extra credit. Perhaps students with higher grades were more willing to accept the additional work of participating, or perhaps those students care more about even small grade boosts. However, the difference in median course grade between participants and non-participants (90% vs. 87.0%) was small enough that we believe that our results likely generalize to the entire class.
Students would have benefitted from more time to complete the assignment
The students were motivated by grades to complete the assignment, but nearly half of students in both conditions did not finish it (Figure 3). Some students reported that they wished the work had been assigned over a longer period of time or earlier in the semester (further from exams). The median participant spent 15 hours on the experiment, which is a bit high for a 12-day homework assignment, and some participants spent significantly more time. If we had allocated more time for the assignment and given it earlier in the semester, perhaps more students would have finished.
Withdrawals were mostly assigned to Bronze
Of the 41 withdrawals, 40 had been assigned to use Bronze. Eight withdrew within 20 minutes of signing up, suggesting they did not make a serious attempt before switching. When withdrawing, 24 students reported that they felt the non-GC version would be easier, perhaps because the GC version required completing an additional part of the assignment. Six said they didn’t understand GC well enough or that it was poorly documented. Eleven students gave no explanation.
We suspect the withdrawal rate would have been lower if we could have convinced participants that they were likely to spend the same amount of time total regardless of which condition they picked. Future experiments could provide incentives that depend on time spent; our design incentivized unbiased time reporting.
Encouraging adoption of safer languages by reducing stress
The apparent decrease over time in how much students like Rust (Figure 6) suggests that if one wants to encourage Rust adoption, changes to the assignment design are needed. It would appear that the Aliasing task had the largest influence on (dis)liking Rust.
It might be surprising that Bronze participants did not like Rust any better than Traditional Rust participants after Aliasing, which Bronze participants completed in about a third of the time of Traditional Rust participants. We observed that stress and frustration correlate more closely with liking Rust than whether the task took longer than participants expected. Expectancy-value theory (Wigfield2000:Expectancy-Value) suggests that an expectation of success contributes to people’s motivation in doing tasks. The theory might suggest that if we want to encourage adoption of Rust and other safe languages, it is more important to provide users with a consistent feeling of progress rather than focusing on minimizing total task completion time. Predictability is beneficial for practicing software engineers as well as for students, and language adopters must first learn the language before using it in a project, so this emphasis is valuable in practice as well as in education.
8. Related Work
Empirical studies have been used to study several programming language design questions, such as static typing (Hanenberg2014:Empirical), lambdas in C++ (Uesbeck2016:Empirical), immutability features (Coblenz2017:Glacier), and typestate (Coblenz2020:Can). Qualitative studies have also been used to understand what factors contribute to users’ perceptions of languages (Coblenz2021:PLIERS). This is the first empirical study of the usability of garbage collection of which we are aware.
Some work investigated how Rust is used in the wild. Astrauskas et al. investigated the use of the unsafe keyword (Astrauskas2020:How), finding that much code relies on unsafe. Fulton et al. (Fulton2021:Benefits) conducted a survey and interviews of Rust programmers to understand their motivations for adopting or not adopting Rust, finding that programmers are motivated by the safety benefits but concerned about the learning curve and challenges in hiring experienced Rust programmers.
Meyerovich investigated programming language adoption (Meyerovich2013:Empirical); developers reported preferring more-expressive languages. Because adding optional garbage collection allows developers to express different kinds of aliasing structures than does Rust alone, adding GC to Rust might make it more likely to be adopted. Zeng and Crichton (Zeng2018:Identifying) investigated forum posts about Rust adoption, hypothesizing that adoption barriers for Rust included poor tool publicity, difficulty solving complex aliasing problems, and integration challenges with existing contexts. GCs, such as Bronze, may help make it easier for programmers to solve complex aliasing problems.
RustViz (Luo2020:Rustviz) is a visualization tool that may help programmers learn Rust ownership semantics.
Cyclone (GrossmanMJHWC02) integrated region-based memory management (tofte97regions), including an optional garbage collector, into a safe dialect of C. Later extensions included support for ownership and borrowing (swamy05experience). Case studies found that similar performance could be obtained if the GC was used judiciously, but that GC can have a significant performance cost if used globally.
9. Conclusions and Future Work
We developed Bronze, a new library-based GC whose goal is to ease the learning and use of Rust. We carried out a randomized, controlled trial of Bronze that showed that it can significantly alleviate some of the challenges posed by the Rust aliasing restrictions for Rust beginners: Bronze participants completed a task that required a complex aliasing structure in about a third as much time as traditional Rust participants. GC may enable Rust programmers, particularly beginners, to complete tasks in much less time.
In the future, we hope to extend the Bronze tracer to trace arbitrary objects that may transitively contain references to GC objects. We also hope to investigate the impact of using GC not just for complex aliasing scenarios, but to mitigate the impact of ownership in general; perhaps doing so could flatten the learning curve and help users feel more positively about Rust.
The experiment is the first (to our knowledge) evaluating the usability benefits of GC. We focused on using GC to relax aliasing restrictions, but GC can also be used to avoid the challenges of reference counting and manual memory allocation. In the future, library-based garbage collection could be used evaluate the usability tradeoffs of garbage collection in other contexts as well.