JET.jl employs Julia's type inference for bug reports.
!!! note
JET.jl needs Julia versions 1.6 and higher;
as such I recommend you give a try on this package with using nightly version
or building Julia from source.
Also note that JET deeply relies on the type inference routine implemented in Julia's compiler,
and so JET analysis result can vary depending on your Julia version.
In general, the newer your Julia version is, your can expect JET to analyze your code more accurately and quickly,
because Julia's compiler is rapidly-advancing, literally day by day.
JET's documentation is now available at here ! Any kind of feedback or help will be very appreciated.
Say you have this strange and buggy file and want to know where to fix:
demo.jl
# demo
# ====
# fibonacci
# ---------
fib(n) = n ≤ 2 ? n : fib(n-1) + fib(n-2)
fib(1000) # never terminates in ordinal execution
fib(m) # undef var
fib("1000") # obvious type error
# language features
# -----------------
# user-defined types
struct Ty{T}
fld::T
end
function foo(a)
v = Ty(a)
return bar(v)
end
# macros will be expanded
@inline bar(n::T) where {T<:Number} = n < 0 ? zero(T) : one(T)
@inline bar(v::Ty{T}) where {T<:Number} = bar(v.fdl) # typo "fdl"
@inline bar(v::Ty) = bar(convert(Number, v.fld))
foo(1.2)
foo("1") # `String` can't be converted to `Number`You can have JET.jl detect possible errors:
julia> using JET
julia> report_and_watch_file("demo.jl"; annotate_types = true)
[toplevel-info] analysis entered into demo.jl
[toplevel-info] analysis from demo.jl finished in 3.455 sec
═════ 4 possible errors found ═════
┌ @ demo.jl:10 fib(m)
│ variable m is not defined: fib(m)
└──────────────
┌ @ demo.jl:11 fib("1000")
│┌ @ demo.jl:7 ≤(n::String, 2)
││┌ @ operators.jl:385 Base.<(x::String, y::Int64)
│││┌ @ operators.jl:336 Base.isless(x::String, y::Int64)
││││ no matching method found for call signature: Base.isless(x::String, y::Int64)
│││└────────────────────
┌ @ demo.jl:32 foo(1.2)
│┌ @ demo.jl:24 bar(v::Ty{Float64})
││┌ @ demo.jl:29 Base.getproperty(v::Ty{Float64}, :fdl::Symbol)
│││┌ @ Base.jl:33 Base.getfield(x::Ty{Float64}, f::Symbol)
││││ type Ty{Float64} has no field fdl
│││└──────────────
┌ @ demo.jl:33 foo("1")
│┌ @ demo.jl:24 bar(v::Ty{String})
││┌ @ demo.jl:30 convert(Number, Base.getproperty(v::Ty{String}, :fld::Symbol)::String)
│││ no matching method found for call signature: convert(Number, Base.getproperty(v::Ty{String}, :fld::Symbol)::String)
││└──────────────Hooray !
JET.jl found possible error points (e.g. MethodError: no method matching isless(::String, ::Int64)) given toplevel call signatures of generic functions (e.g. fib("1000")).
Note that JET can find these errors while demo.jl is so inefficient (especially the fib implementation) that it would never terminate in actual execution.
That is possible because JET analyzes code only on type level.
This technique is often called "abstract interpretation" and JET internally uses Julia's native type inference implementation, so it can analyze code as fast/correctly as Julia's code generation.
Lastly let's apply the following diff to demo.jl so that it works nicely:
fix-demo.jl.diff
diff --git a/demo.jl b/demo-fixed.jl
index d2b188a..1d1b3da 100644
--- a/demo.jl
+++ b/demo.jl
@@ -5,11 +5,21 @@
# fibonacci
# ---------
-fib(n) = n ≤ 2 ? n : fib(n-1) + fib(n-2)
+# cache, cache, cache
+function fib(n::T) where {T<:Number}
+ cache = Dict(zero(T)=>zero(T), one(T)=>one(T))
+ return _fib(n, cache)
+end
+_fib(n, cache) = if haskey(cache, n)
+ cache[n]
+else
+ cache[n] = _fib(n-1, cache) + _fib(n-2, cache)
+end
-fib(1000) # never terminates in ordinal execution
-fib(m) # undef var
-fib("1000") # obvious type error
+fib(BigInt(1000)) # will terminate in ordinal execution as well
+m = 1000 # define m
+fib(m)
+fib(parse(Int, "1000"))
# language features
@@ -27,8 +37,8 @@ end
# macros will be expanded
@inline bar(n::T) where {T<:Number} = n < 0 ? zero(T) : one(T)
-@inline bar(v::Ty{T}) where {T<:Number} = bar(v.fdl) # typo "fdl"
+@inline bar(v::Ty{T}) where {T<:Number} = bar(v.fld) # typo fixed
@inline bar(v::Ty) = bar(convert(Number, v.fld))
foo(1.2)
-foo("1") # `String` can't be converted to `Number`
+foo('1') # `Char` will be converted to `UInt32`If you apply the diff (i.e. update and save the demo.jl), JET will automatically re-trigger analysis, and this time, won't complain anything:
git apply fix-demo.jl.diff
[toplevel-info] entered into demo.jl
[toplevel-info] exited from demo.jl (took 3.423 sec)
No errors !- release v0.1.0: see aviatesk#121
- provide editor/IDE integrations for "watch" mode (aviatesk#85 will be a starting point)
- support package analysis (issue aviatesk#76, something like PR aviatesk#101 can be a starting point)
- implement a "global" abstract interpretation routine (issue aviatesk#113)
- performance linting (report performance pitfalls, i.e. report an error when there're too many methods matched)
- ideally, I want to extend JET.jl to provide some of LSPs other than diagnostics, e.g. providers of completions, rename refactor, etc.
This project started as my grad thesis project at Kyoto University, supervised by Prof. Takashi Sakuragawa. We were heavily inspired by ruby/typeprof, an experimental type understanding/checking tool for Ruby. The grad thesis about this project is published at https://github.com/aviatesk/grad-thesis, but currently it's only available in Japanese.