Language Quality

The eleven workload scores sum to 1,067.0631. Their unweighted mean is 97.01/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB09 (Statistics), 100.00; its lowest is MB06 (Matrix multiplication), 84.10. These measured differences, after workload-by-workload normalization, determine the total.

The eleven workload scores sum to 1,048.4662. Their unweighted mean is 95.32/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB10 (File I/O), 100.00; its lowest is MB04 (Floating-point arithmetic), 76.70. These measured differences, after workload-by-workload normalization, determine the total.

The eleven workload scores sum to 1,100. Their unweighted mean is 100.00/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB01 (Fibonacci), 100.00; its lowest is MB01 (Fibonacci), 100.00. These measured differences, after workload-by-workload normalization, determine the total.

The archived MB-00 value is 0.001247468 s. The cohort minimum is 0.001247468 s, so 100 × 0.001247468 ÷ 0.001247468 = 100.00.

The eleven workload scores sum to 1,075.9355. Their unweighted mean is 97.81/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB05 (Vector inner product), 100.00; its lowest is MB11 (Collections), 76.99. These measured differences, after workload-by-workload normalization, determine the total.

The eleven workload scores sum to 1,046.4657. Their unweighted mean is 95.13/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB01 (Fibonacci), 100.00; its lowest is MB02 (Factorial), 78.71. These measured differences, after workload-by-workload normalization, determine the total.

The eleven workload scores sum to 1,100. Their unweighted mean is 100.00/100. Every value and corresponding score contribution is shown below.

Its highest contribution is MB01 (Fibonacci), 100.00; its lowest is MB01 (Fibonacci), 100.00. These measured differences, after workload-by-workload normalization, determine the total.

The archived MB-00 value is 11,644,928 bytes. The cohort minimum is 11,612,160 bytes, so 100 × 11,612,160 ÷ 11,644,928 = 99.72.

The recorded levels are 4 + 4 + 4 + 3 + 4 = 19 out of 20 level units. The runner's conversion gives 19 × 5 = 95.00/100.

Recorded judgment for Declaration ceremony, Control data ceremony, Abstraction ceremony, Error resource ceremony, Semantic work ratio: No cross-language byte/line-count comparison table was produced in this shard (assigned languages = Kotlin only); comparison is against idiomatic-best-practice expectation for the same frozen algorithm as required by language_quality.md fairness rules, not against a fabricated multi-language corpus. | Qualitative + structural analysis of sampled Kotlin micro-benchmark and adversarial programs under template/programs/kotlin/{micro,adversarial}. Assessed token/line density needed to express the same algorithmic workload relative to idiomatic Kotlin best practices (no artificial verbosity or golfing). | Kotlin's type inference (val/var without explicit types), default/named arguments, single-expression functions, and data classes (e.g.

The recorded levels are 4 + 4 + 4 + 3 + 4 = 19 out of 20 level units. The runner's conversion gives 19 × 5 = 95.00/100.

Recorded judgment for Local intent visibility, Structural clarity, Consistency regular forms, Name type signal, Control effect traceability: Static inspection of the same Go source corpus for naming clarity, structural regularity, comment density, and syntactic noise. | gofmt-enforced canonical formatting yields uniform layout across all files, a strong readability asset from tooling-enforced single style. | Small function bodies and single level of abstraction per function make control flow easy to trace. | Explicit types everywhere reduce ambiguity for readers at the cost of some verbosity, net positive for numeric code readability. | Clear doc comments precede important functions explaining algorithmic intent. | No operator overloading or macro/metaprogramming; code executes exactly as written with no hidden control flow. | Multiple return values and consistent error-checking idiom aid pattern recognition despite added line count. | Lack of pattern matching/algebraic data types forces if/else and switch dispatch in complex adversarial samples, slightly less readable than exhaustive pattern matches elsewhere.

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Data modeling, Abstraction reuse, Higher order composition, Systems runtime control, Feature composability: Qualitative structural review of Rust language surface using template/programs/rust/adversarial and template/programs/rust/micro reference programs (ADV-01..ADV-26, mb00..mb11) as the concrete workload set common to all languages under evaluation. | Rust supports algebraic data types (enums with payload variants), pattern matching with exhaustiveness checking, trait-based polymorphism (static and dynamic dispatch via dyn Trait), generics with trait bounds, closures with capture-by-move/borrow, RAII-based resource management, zero-cost abstractions (iterators compiling to loops), and a strong ownership/borrowing model expressed directly in the type system (e.g.

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Location context, Cause specificity, Expected actual, Machine stability, Actionability: rustc via rustup lacked a default toolchain in this sandbox and 'rustup default stable' is not in the permitted executable allowlist, preventing a fresh compiler-output capture for this run. | Direct compiler invocation attempted (rustc test_diag.rs) plus qualitative review of known rustc diagnostic behavior against a deliberately erroneous 5-line program containing a type mismatch (&str assigned to i32) and an out-of-bounds vector index. | Toolchain execution note: the sandbox's rustc is a rustup shim with no default toolchain configured, and 'rustup' is outside the allowed executable list, so direct compiler invocation in this sandbox returned an infrastructure error ('rustup could not choose a version of rustc to run') rather than a language diagnostic.

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Module import model, Resolution determinism, Dependency declaration, Isolation namespacing, Transitive visibility: Assessment of language-level dependency semantics for Rust, independent of Cargo ecosystem maturity (which belongs to Ecosystem evaluation). | Rust programs reviewed under /quidra-benchmark/template/programs/rust/micro (mb00.rs through mb11.rs) are self-contained, relying only on the Rust standard library (std::collections, std::io, std::fmt, etc.) with no external crate dependencies required for core micro-benchmark logic. | Rust's module system (mod, use, pub) provides explicit, unambiguous dependency declaration at the language level: every external symbol used must be explicitly brought into scope via use, and crate dependencies are declared once in Cargo.toml with semantic versioning enforced by Cargo.lock rather than manual header/linker bookkeeping. | No global mutable namespace pollution: Rust has no implicit global includes or preprocessor-based conditional dependency inclusion (unlike C/C++ #include and #ifdef chains), reducing accidental coupling. | Cargo's dependency resolution is deterministic and reproducible via…

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Specified core semantics, Platform abstraction, Data numeric portability, Conditional platform mechanism, Artifact source separation: Assessed via static inspection of provided sample sources and documented Go toolchain/language design; did not execute cross-compilation in this sandbox session. | Static language-design analysis of Go's cross-platform build model and inspection of sampled program sources for OS/architecture-specific constructs. | Sampled sources (mb00..mb11) use only standard, platform-neutral packages (fmt, math, time, sync); no cgo, no syscall-specific code, no OS-gated build tags. | Go toolchain supports first-party cross-compilation to dozens of GOOS/GOARCH targets from a single host via env vars understood by the compiler itself -- a language/toolchain design property. | Language spec and runtime (goroutine scheduler, GC, memory model) abstract OS differences uniformly; only explicit syscall/cgo use introduces platform coupling, none present in samples. | Static linking by default (CGO_ENABLED=0) reduces dynamic-library coupling to host distributions.

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Foreign declaration, ABI calling control, Type layout mapping, Ownership lifetime boundary, Bidirectional callbacks: Rust provides a stable, zero-cost extern "C" FFI mechanism directly in the language, with #[repr(C)] for controlling struct layout to match C ABI exactly. | unsafe blocks explicitly and locally scope the parts of code that cross the safety boundary into foreign code, keeping the rest of the program's soundness guarantees intact and auditable. | Bidirectional FFI is supported: Rust can call into C libraries (via extern "C" { fn ... } declarations and linking) and can also export Rust functions to C callers (via #[no_mangle] pub extern "C" fn). | No garbage collector or runtime is required to call/be called from C, unlike managed languages (Java/Kotlin JNI, Go cgo) that must cross a runtime boundary with extra marshalling and GC-interaction concerns. | Ownership/borrowing rules extend conceptually across FFI: raw pointers (*const T, *mut T) are the FFI currency, and the type system still helps callers reason about aliasing even though the compiler cannot enforce Rust's guarantees across the boundary. | bindgen and cbindgen (ecosystem…

The recorded levels are 4 + 4 + 4 + 4 + 4 = 20 out of 20 level units. The runner's conversion gives 20 × 5 = 100.00/100.

Recorded judgment for Parallel execution, Async structured, Synchronization channels, Race safety, Error cancel propagation: Swift's concurrency design (structured concurrency + actors + compile-time Sendable/data-race checking) is one of the more advanced and safety-oriented concurrency designs among mainstream languages, comparable in ambition to Rust's ownership-based concurrency safety but achieved with less ceremony, though the ecosystem-wide transition to strict concurrency is still incomplete (an ecosystem/maturity concern excluded here). | Swift | Swift has a modern, language-integrated structured concurrency model (async/await, Task, TaskGroup, AsyncSequence) introduced in Swift 5.5, built into the compiler and standard library rather than bolted on as a library. | Actors provide a language-level data-race-avoidance primitive: actor-isolated state is checked at compile time, preventing many classes of shared-mutable-state races without runtime locks in common cases. | Sendable checking and strict concurrency checking (progressively enforced, mandatory by default in Swift 6 language mode) give compile-time diagnostics for data races crossing…

The retained runner result assigns 67.08 under the equal blend of type-hazard stage credit and static-guarantee coverage. It does not preserve the separate TS_stage and TS_static values, so the share of this score attributable to each component cannot be recovered from the committed summary.

The published integrity-preservation rate is 100.00%. The formula rewards the implementation's intervention on storage hazards, including safe runtime diagnosis; an uncontrolled crash receives no integrity-preservation credit. The compact evidence does not identify which hazards received credit.

The runner reports 68.75% safe-or-prevented row-weight among the applicable weight reaching execution. The retained result omits the numerator and denominator, so this percentage cannot establish how many cases reached runtime or which checks account for it.

The recorded 65.91 is detection-stage credit averaged over the applicable cases in the prescribed 11-hazard set. More prevention or earlier diagnosis earns more credit, while crashes and plausible wrong results earn less. The per-case stages needed to identify this language's particular contributing failures were not retained in the repository summary.

The recorded 85.71 is detection-stage credit averaged over the applicable cases in the prescribed 7-hazard set. More prevention or earlier diagnosis earns more credit, while crashes and plausible wrong results earn less. The per-case stages needed to identify this language's particular contributing failures were not retained in the repository summary.

The recorded 65.00 is detection-stage credit averaged over the applicable cases in the prescribed 26-hazard set. More prevention or earlier diagnosis earns more credit, while crashes and plausible wrong results earn less. The per-case stages needed to identify this language's particular contributing failures were not retained in the repository summary.

The retained mechanical result is 68.59 for the diagnostic-evidence proxy. The source implementation rewards hazard identification, source locations and stable fault keys, but the compact record omits their individual A1/A2/A3 means and the diagnostic reports needed to attribute the exact difference.

The recorded 88.24 corresponds to approximately 11.76% of applicable row-weight classified as a silent bug or undefined behavior, using the complement formula. This is a weighted aggregate, not a count of independently observed production bugs.

The published 100.00 corresponds to approximately 0.00% flagged toolchain-failure row-weight. A full score here reports that narrow aggregate; runtime crashes or incorrect outputs are assessed by other requirements.