5 Automated Testing Benchmark🔗ℹ

Redex’s automated testing benchmark provides a collection of buggy models and falsifiable properties to test how efficiently methods of automatic test case generation are able to find counterexamples for the bugs.

Each entry in the benchmark contains a check function and multiple generate functions. The check function determines if a given example is a counterexample (i.e. if it uncovers the buggy behavior) and each of the generate functions generates candidate examples to be tried. There are multiple ways to generate terms for each model. They typically correspond to different uses of generate-term, but could be any way to generate examples. See run-gen-and-check for the precise contracts for generate and check functions.

Most of the entries in the benchmark are small differences to existing, bug-free models, where some small change to the model introduces the bug. These changes are described using define-rewrite.

To run a benchmark entry with a particular generator, see run-gen-and-check/mods.

5.1 The Benchmark Models🔗ℹ

The programs in our benchmark come from two sources: synthetic examples based on our experience with Redex over the years and from models that we and others have developed and bugs that were encountered during the development process.

The benchmark has six different Redex models, each of which provides a grammar of terms for the model and a soundness property that is universally quantified over those terms. Most of the models are of programming languages and most of the soundness properties are type-soundness, but we also include red-black trees with the property that insertion preserves the red-black invariant, as well as one richer property for one of the programming language models (discussed in stlc-sub).

For each model, we have manually introduced bugs into a number of copies of the model, such that each copy is identical to the correct one, except for a single bug. The bugs always manifest as a term that falsifies the soundness property.

The table in figure 1 gives an overview of the benchmark suite, showing some numbers for each model and bug. Each model has its name and the number of lines of code for the bug-free model (the buggy versions are always within a few lines of the originals). The line number counts include the model and the specification of the property.

Each bug has a number and, with the exception of the rvm model, the numbers count from 1 up to the number of bugs. The rvm model bugs are all from Klein et al. (2013)’s work and we follow their numbering scheme (see rvm for more information about how we chose the bugs from that paper).

The size column shows the size of the term representing the smallest counterexample we know for each bug, where we measure size as the number of pairs of parentheses and atoms in the s-expression representation of the term.

Each subsection of this section introduces one of the models in the benchmark, along with the errors we introduced into each model.

Model		LoC		Bug#		S/M/D/U		Size		Description of Bug
stlc		211		1		S		3		app rule the range of the function is matched to the argument
				2		M		5		the ((cons v) v) value has been omitted
				3		S		8		the order of the types in the function position of application has been swapped
				4		S		9		the type of cons is incorrect
				5		S		7		the tail reduction returns the wrong value
				6		M		7		hd reduction acts on partially applied cons
				7		M		9		evaluation isn't allowed on the rhs of applications
				8		U		12		lookup always returns int
				9		S		15		variables aren't required to match in lookup
poly-stlc		280		1		S		6		app rule the range of the function is matched to the argument
				2		M		11		the (([cons @ τ] v) v) value has been omitted
				3		S		14		the order of the types in the function position of application has been swapped
				4		S		15		the type of cons is incorrect
				5		S		16		the tail reduction returns the wrong value
				6		M		16		hd reduction acts on partially applied cons
				7		M		9		evaluation isn't allowed on the rhs of applications
				8		U		15		lookup always returns int
				9		S		18		variables aren't required to match in lookup
stlc-sub		241		1		S		8		forgot the variable case
				2		S		13		wrong order of arguments to replace call
				3		S		10		swaps function and argument position in application
				4		D		22		variable not fresh enough
				5		SM		17		replace all variables
				6		S		8		forgot the variable case
				7		S		13		wrong order of arguments to replace call
				8		S		10		swaps function and argument position in application
				9		SM		17		replace all variables
let-poly		662		1		S		8		use a lambda-bound variable where a type variable should have been
				2		D		28		the classic polymorphic let + references bug
				3		M		3		mix up types in the function case
				4		S		8		dropped the occurs check
				5		M		3		eliminate-G was written as if it always gets a Gx as input
				6		M		8		copy and paste error in the orient → rule
				7		D		12		used let --> left-left-λ rewrite rule for let, but the right-hand side is less polymorphic
list-machine		256		1		S		22		confuses the lhs value for the rhs value in cons type rule
				2		M		22		var-set may skip a var with matching id (in reduction)
				3		S		29		cons doesn't actually update the store
rbtrees		187		1		M		13		ins does no rebalancing
				2		M		15		the first case is removed from balance
				3		S		51		doesn't increment black depth in non-empty case
delim-cont		287		1		M		46		guarded mark reduction doesn't wrap results with a list/c
				2		M		25		list/c contracts aren't applied properly in the cons case
				3		S		52		the function argument to call/comp has the wrong type
rvm		712		2		M		24		stack offset / pointer confusion
				3		D		33		application slots not initialized properly
				4		M		17		mishandling branches when then branch needs more stack than else branch; bug in the boxenv case not checking a stack bound
				5		M		23		mishandling branches when then branch needs more stack than else branch; bug in the let-rec case not checking a stack bound
				6		M		15		forgot to implement the case-lam branch in verifier
				14		M		27		certain updates to initialized slots could break optimizer assumptions
				15		S		21		neglected to restrict case-lam to accept only 'val' arguments

Figure 1: Benchmark Overview

5.1.1 stlc🔗ℹ

A simply-typed λ-calculus with base types of numbers and lists of numbers, including the constants +, which operates on numbers, and cons, head, tail, and nil (the empty list), all of which operate only on lists of numbers. The property checked is type soundness: the combination of preservation (if a term has a type and takes a step, then the resulting term has the same type) and progress (that well-typed non-values always take a reduction step).

We introduced nine different bugs into this system. The first confuses the range and domain types of the function in the application rule, and has the small counterexample: (hd 0). We consider this to be a shallow bug, since it is essentially a typo and it is hard to imagine anyone with any knowledge of type systems making this conceptual mistake. Bug 2 neglects to specify that a fully applied cons is a value, thus the list ((cons 0) nil) violates the progress property. We consider this be be a medium bug, as it is not a typo, but an oversight in the design of a system that is otherwise correct in its approach.

We consider the next three bugs to be shallow. Bug 3 reverses the range and the domain of function types in the type judgment for applications. This was one of the easiest bug for all of our approaches to find. Bug 4 assigns cons a result type of int. The fifth bug returns the head of a list when tl is applied. Bug 6 only applies the hd constant to a partially constructed list (i.e., the term (cons 0) instead of ((cons 0) nil)). Only the grammar based random generation exposed bugs 5 and 6 and none of our approaches exposed bug 4.

The seventh bug, also classified as medium, omits a production from the definition of evaluation contexts and thus doesn’t reduce the right-hand-side of function applications.

Bug 8 always returns the type int when looking up a variable’s type in the context. This bug (and the identical one in the next system) are the only bugs we classify as unnatural. We included it because it requires a program to have a variable with a type that is more complex that just int and to actually use that variable somehow.

Bug 9 is simple; the variable lookup function has an error where it doesn’t actually compare its input to variable in the environment, so it effectively means that each variable has the type of the nearest enclosing lambda expression.

5.1.2 poly-stlc🔗ℹ

This is a polymorphic version of stlc, with a single numeric base type, polymorphic lists, and polymorphic versions of the list constants. No changes were made to the model except those necessary to make the list operations polymorphic. There is no type inference in the model, so all polymorphic terms are required to be instantiated with the correct types in order for the function to type check. Of course, this makes it much more difficult to automatically generate well-typed terms, and thus counterexamples. As with stlc, the property checked is type soundness.

All of the bugs in this system are identical to those in stlc, aside from any changes that had to be made to translate them to this model.

This model is also a subset of the language specified in Pałka et al. (2011), who used a specialized and optimized QuickCheck generator for a similar type system to find bugs in GHC. We adapted this system (and its restriction in stlc) because it has already been used successfully with random testing, which makes it a reasonable target for an automated testing benchmark.

5.1.3 stlc-sub🔗ℹ

The same language and type system as stlc, except that in this case all of the errors are in the substitution function.

Our own experience has been that it is easy to make subtle errors when writing substitution functions, so we added this set of tests specifically to target them with the benchmark. There are two soundness checks for this system. Bugs 1-5 are checked in the following way: given a candidate counterexample, if it type checks, then all βv-redexes in the term are reduced (but not any new ones that might appear) using the buggy substitution function to get a second term. Then, these two terms are checked to see if they both still type check and have the same type and that the result of passing both to the evaluator is the same.

Bugs 4-9 are checked using type soundness for this system as specified in the discussion of the stlc model. We included two predicates for this system because we believe the first to be a good test for a substitution function but not something that a typical Redex user would write, while the second is something one would see in most Redex models but is less effective at catching bugs in the substitution function.

The first substitution bug we introduced simply omits the case that replaces the correct variable with the term to be substituted. We considered this to be a shallow error, and indeed all approaches were able to uncover it, although the time it took to do so varied.

Bug 2 permutes the order of arguments when making a recursive call. This is also categorized as a shallow bug, although it is a common one, at least based on our experience writing substitutions in Redex.

Bug 3 swaps the function and argument positions of an application while recurring, again essentially a typo and a shallow error, although one of the more difficult to find in this model.

The fourth substitution bug neglects to make the renamed bound variable fresh enough when recurring past a lambda. Specifically, it ensures that the new variable is not one that appears in the body of the function, but it fails to make sure that the variable is different from the bound variable or the substituted variable. We categorized this error as deep because it corresponds to a misunderstanding of how to generate fresh variables, a central concern of the substitution function.

Bug 5 carries out the substitution for all variables in the term, not just the given variable. We categorized it as SM, since it is essentially a missing side condition, although a fairly egregious one.

Bugs 6-9 are duplicates of bugs 1-3 and bug 5, except that they are tested with type soundness instead. (It is impossible to detect bug 4 with this property.)

5.1.4 let-poly🔗ℹ

A language with ML-style let polymorphism, included in the benchmark to explore the difficulty of finding the classic let+references unsoundness. With the exception of the classic bug, all of the bugs were errors made during the development of this model (and that were caught during development).

The first bug is simple; it corresponds to a typo, swapping an x for a y in a rule such that a type variable is used as a program variable.

Bug number 2 is the classic let+references bug. It changes the rule for let-bound variables in such a way that generalization is allowed even when the initial value expression is not a value.

Bug number 3 is an error in the function application case where the wrong types are used for the function position (swapping two types in the rule).

Bugs 4, 5, and 6 were errors in the definition of the unification function that led to various bad behaviors.

Finally, bug 7 is a bug that was introduced early on, but was only caught late in the development process of the model. It used a rewriting rule for let expressions that simply reduced them to the corresponding ((λ expressions. This has the correct semantics for evaluation, but the statement of type-soundness does not work with this rewriting rule because the let expression has more polymorphism that the corresponding application expression.

5.1.5 list-machine🔗ℹ

An implementation of Appel et al. (2012)’s list-machine benchmark. This is a reduction semantics (as a pointer machine operating over an instruction pointer and a store) and a type system for a seven-instruction first-order assembly language that manipulates cons and nil values. The property checked is type soundness as specified in Appel et al. (2012), namely that well-typed programs always step or halt. Three mutations are included.

The first list-machine bug incorrectly uses the head position of a cons pair where it should use the tail position in the cons typing rule. This bug amounts to a typo and is classified as simple.

The second bug is a missing side-condition in the rule that updates the store that has the effect of updating the first position in the store instead of the proper position in the store for all of the store update operations. We classify this as a medium bug.

The final list-machine bug is a missing subscript in one rule that has the effect that the list cons operator does not store its result. We classify this as a simple bug.

5.1.6 rbtrees🔗ℹ

A model that implements the red-black tree insertion function and checks that insertion preserves the red-black tree invariant (and that the red-black tree is a binary search tree).

The first bug simply removes the re-balancing operation from insert. We classified this bug as medium since it seems like the kind of mistake that a developer might make in staging the implementation. That is, the re-balancing operation is separate and so might be put off initially, but then forgotten.

The second bug misses one situation in the re-balancing operation, namely when a black node has two red nodes under it, with the second red node to the right of the first. This is a medium bug.

The third bug is in the function that counts the black depth in the red-black tree predicate. It forgets to increment the count in one situation. This is a simple bug.

5.1.7 delim-cont🔗ℹ

Takikawa et al. (2013)’s model of a contract and type system for delimited control. The language is Plotkin’s PCF extended with operators for delimited continuations, continuation marks, and contracts for those operations. The property checked is type soundness. We added three bugs to this model.

The first was a bug we found by mining the model’s git repository’s history. This bug fails to put a list contract around the result of extracting the marks from a continuation, which has the effect of checking the contract that is supposed to be on the elements of a list against the list itself instead. We classify this as a medium bug.

The second bug was in the rule for handling list contracts. When checking a contract against a cons pair, the rule didn’t specify that it should apply only when the contract is actually a list contract, meaning that the cons rule would be used even on non-list contacts, leading to strange contract checking. We consider this a medium bug because the bug manifests itself as a missing list/c in the rule.

The last bug in this model makes a mistake in the typing rule for the continuation operator. The mistake is to leave off one-level of arrows, something that is easy to do with so many nested arrow types, as continuations tend to have. We classify this as a simple error.

5.1.8 rvm🔗ℹ

A existing model and test framework for the Racket virtual machine and bytecode verifier (Klein et al. 2013). The bugs were discovered during the development of the model and reported in section 7 of that paper. Unlike the rest of the models, we do not number the bugs for this model sequentially but instead use the numbers from Klein et al. (2013)’s work.

These bugs are described in detail in Klein et al. (2013)’s paper.

This model is unique in our benchmark suite because it includes a function that makes terms more likely to be useful test cases. In more detail, the machine model does not have variables, but instead is stack-based; bytecode expressions also contain internal pointers that must be valid. Generating a random (or in-order) term is relatively unlikely to produce one that satisfies these constraints. For example, of the first 10,000 terms produced by the in-order enumeration only 1625 satisfy the constraints. The ad hoc random generator generators produces about 900 good terms in 10,000 attempts and the uniform random generator produces about 600 in 10,000 attempts.

To make terms more likely to be good test cases, this model includes a function that looks for out-of-bounds stack offsets and bogus internal pointers and replaces them with random good values. This function is applied to each of the generated terms before using them to test the model.

5.2 Managing Benchmark Modules🔗ℹ

This section describes utilities for making changes to existing modules to create new ones, intended to assist in adding bugs to models and keeping buggy models in sync with changes to the original model.

syntax (define-rewrite id from ==> to   [#:context (context-id ...)    #:variables (variable-id ...)    #:once-only    #:exactly-once])

Defines a syntax transformer bound to id, the effect of which is to rewrite syntax matching the pattern from to the result expression to. The from argument should follow the grammar of a syntax-case pattern, and to acts as the corresponding result expression. The behavior of the match is the same as syntax-case, except that all identifiers in from are treated as literals with the exception of an identifier that has the same binding as a variable-id appearing in the #:variables keyword argument, which is treated as a pattern variable. (The reverse of the situation for syntax-case, where literals must be specified instead.) The rewrite will only be applied in the context of a module form, but it will be applied wherever possible within the module body, subject to a few constraints.

The rest of the keyword arguments control where and how often the rewrite may be applied. The #:once-only option specifies that the rewrite can be applied no more than once, and the #:exactly-once option asserts that the rewrite must be applied once (and no more). In both cases a syntax error is raised if the condition is not met. The #:context option searches for syntax of the form (some-id . rest), where the binding of some-id matches that of the first context-id in the #:context list, at which point it recurs on rest but drops the first id from the list. Once every context-id has been matched, the rewrite can be applied.

5.3 Running Benchmark Models🔗ℹ

The get-gen thunk is called to build a generator of random terms (which may close over some state). A new generator is created each time the property is found to be false.

Each generated term is passed to check to see if it is a counterexample. The interval in milliseconds between counterexamples is tracked, and the process is repeated either until the time specified by seconds has elapsed or the standard error in the average interval between counterexamples is less than 10% of the average.

The result is an instance of run-results containing the total number of terms generated, the total elapsed time, and the number of counterexamples found. More detailed information can be obtained using the benchmark logging facilities, for which name is refers to the name of the model, and type is a symbol indicating the generation type used.

5.4 Logging🔗ℹ

Detailed information gathered during a benchmark run is logged to the current-logger, at the 'info level, with the message "BENCHMARK-LOGGING". The data field of the log message contains a bmark-log-data struct, which wraps data of the form:

Where event is a symbol that designates the type of event, and timestamp is symbol that contains the current-date of the event in ISO-8601 format. The information in data-list depends on the event, but must be in the form of a list alternating between a keyword and a datum, where the keyword is a short description of the datum.

5.5 Plotting🔗ℹ

Plotting and analysis tools consume data of the form produced by the benchmark logging facilities (see Logging).

TODO!

5.6 Finding the Benchmark Models🔗ℹ

The models included in the distribution of the benchmark are in the "redex/benchmark/models" subdirectory of the redex-benchmark package. In addition to the redex/benchmark/models/all-info library documented here, each such subdirectory contains an info file named according to the pattern "<name>-info.rkt", defining a module that provides a model-specific all-mods function.

A command line interface is provided by the file "redex/benchmark/run-benchmark.rkt", which takes an “info” file as described above as its primary argument and provides options for running the listed tests. It automatically writes results from each run to a separate log file, all of which are located in a temporary directory. (The directory path is printed to standard out at the beginning of the run).