\section{Constraint generation}\label{chapter:constraintGeneration} % Our type inference algorithm is split into two parts. % A constraint generation step \textbf{TYPE} and a \unify{} step. % Method names are not unique. % It is possible to define the same method in multiple classes. % The \TYPE{} algorithm accounts for that by generating Or-Constraints. % This can lead to multiple possible solutions. %\subsection{Well-Formedness} % But it can be easily adapted to Featherweight Java or Java. % We add T <. a for every return of an expression anyway. If anything returns a Generic like X it is not directly used in a method call like X A head(List l){ ... } List get() { ... } } class Class2{ example(c1){ return c1.head(c1.get()); } } \end{verbatim} %This example comes with predefined type annotations. We assume the class \texttt{Class1} has already been processed by our type inference algorithm leading to the following type annotations: %Now we call the $\fjtype{}$ function with the class \texttt{Class2} and the method assumptions for the preceeding class: \begin{displaymath} \mtypeEnvironment = \left\{\begin{array}{l} \texttt{m}: \generics{\type{A} \triangleleft \type{Object}} \ (\type{Class1},\, \exptype{List}{\type{A}}) \to \type{X}, \\ \texttt{get}: (\type{Class1}) \to \wctype{\wildcard{A}{\type{Object}}{\type{String}}}{List}{\rwildcard{A}} \end{array} \right\} \end{displaymath} At first we have to convert the example method to a syntactically correct \TamedFJ{} program. Afterwards the the \fjtype{} algorithm is able to generate constraints. \begin{minipage}{0.45\textwidth} \begin{lstlisting}[style=tamedfj] class Class2 { example(c1) = let x = c1 in let xp = x.get() in x.m(xp); } \end{lstlisting} \end{minipage}% \hfill \begin{minipage}{0.5\textwidth} \begin{constraintset} $ \begin{array}{l} \ntv{c1} \lessdot \ntv{x}, \ntv{x} \lessdotCC \type{Class1}, \\ \ntv{c1} \lessdot \ntv{x}, \ntv{x} \lessdotCC \type{Class1}, \\ \wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}} \lessdot \tv{xp}, \\ \tv{xp} \lessdotCC \exptype{List}{\wtv{a}} \end{array} $ \end{constraintset} \end{minipage} Following is a possible solution for the given constraint set: \begin{minipage}{0.55\textwidth} \begin{lstlisting}[style=letfj] class Class2 { example(c1) = let x : Class1 = c1 in let xp : (*@$\wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}}$@*) = x.get() in x.m(xp); } \end{lstlisting} \end{minipage}% \hfill \begin{minipage}{0.4\textwidth} \begin{constraintset} $ \begin{array}{l} \sigma(\ntv{x}) = \type{Class1} \\ %\tv{xp} \lessdot \exptype{List}{\wtv{x}}, \\ %\exptype{List}{\type{String}} \lessdot \tv{p1}, \\ \sigma(\tv{xp}) = \wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}} \\ \end{array} $ \end{constraintset} \end{minipage} For $\wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}}$ to be a correct solution for $\tv{xp}$ the constraint $\wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}} \lessdotCC \exptype{List}{\wtv{a}}$ must be satisfied. This is possible, because we deal with a capture constraint. The $\lessdotCC$ constraint allows the left side to undergo a capture conversion which leads to $\exptype{List}{\rwildcard{A}} \lessdot \exptype{List}{\wtv{a}}$. Now a substitution of the wildcard placeholder $\wtv{a}$ with $\rwildcard{A}$ leads to a satisfied constraint set. The wildcard placeholders are not used as parameter or return types of methods. Or as types for variables introduced by let statements. They are only used for generic method parameters during a method invocation. Type placeholders which are not flagged as wildcard placeholders ($\wtv{a}$) can never hold a free variable or a type containing free variables. This practice hinders free variables to leave their scope. The free variable $\rwildcard{A}$ generated by the capture conversion on the type $\wctype{\wildcard{A}{\type{String}}{\bot}}{List}{\rwildcard{A}}$ cannot be used anywhere else then inside the constraints generated by the method call \texttt{x.m(xp)}. \begin{displaymath} \begin{array}{@{}l@{}l} \typeExpr{} &({\mtypeEnvironment} , e_1 \elvis{} e_2, \tv{a}) = \\ & \begin{array}{ll} \textbf{let} & \tv{r}_1, \tv{r}_2 \ \text{fresh} \\ & \consSet_1 = \typeExpr({\mtypeEnvironment}, e_1, \tv{r}_2)\\ & \consSet_2 = \typeExpr({\mtypeEnvironment}, e_2, \tv{r}_2)\\ {\mathbf{in}} & { \consSet_1 \cup \consSet_2 \cup \set{\tv{r}_1 \lessdot \tv{a}, \tv{r}_2 \lessdot \tv{a}}} \end{array} \end{array} \end{displaymath} %We could skip wfresh here: \begin{displaymath} \begin{array}{@{}l@{}l} \typeExpr{} &({\mtypeEnvironment} , x, \tv{a}) = \mtypeEnvironment(x) \end{array} \end{displaymath} \begin{displaymath} \begin{array}{@{}l@{}l} \typeExpr{} &({\mtypeEnvironment} , \texttt{new}\ \type{C}(\overline{e}), \tv{a}) = \\ & \begin{array}{ll} \textbf{let} & \ol{\tv{r}} \ \text{fresh} \\ & \overline{\consSet} = \typeExpr({\mtypeEnvironment}, \overline{e}, \ol{\tv{r}}) \\ & C = \set{\ol{\tv{r}} \lessdot [\ol{\tv{a}}/\ol{X}]\ol{T}, \ol{\tv{a}} \lessdot \ol{N} \mid \texttt{class}\ \exptype{C}{\ol{X} \triangleleft \ol{N}} \set{ \ol{T\ f}; \ldots}} \\ {\mathbf{in}} & { \overline{\consSet} \cup \set{\tv{a} \doteq \exptype{C}{\ol{a}}}} \end{array} \end{array} \end{displaymath} % Problem: % > void t2(List l){} % void test(List> l){ % t2(l); % } % Problem: % List> <. List, a <. List % Y.List =. a % Z.List <. List % These constraints should fail! % \section{Result Generation} % If \unify{} returns atleast one type solution $(\Delta, \sigma)$ % the last step of the type inference algorithm is to generate a typed class. % This section presents our type inference algorithm. % The algorithm is given method assumptions $\mv\Pi$ and applied to a % single class $\mv L$ at a time: % \begin{gather*} % \fjtypeinference(\mtypeEnvironment, \texttt{class}\ \exptype{C}{\ol{X} % \triangleleft \ol{N}} \triangleleft \type{N}\ \{ \overline{\type{T} \ f};\ \ol{M} \}) = \\ % \quad \quad \begin{array}[t]{rll} % \textbf{let}\ % (\overline{\methodAssumption}, \consSet) &= \fjtype{}(\mv{\Pi}, \texttt{class}\ \exptype{C}{\ol{X} % \triangleleft \ol{N}} \triangleleft \type{N}\ \{ \ldots \}) & % \text{// constraint generation}\\ % {(\Delta, \sigma)} &= \unify{}(\consSet,\, \ol{X} <: \ol{N}) & \text{// constraint solving}\\ % \generics{\ol{Y} \triangleleft \ol{S}} &= \set{ \type{Y} \triangleleft \type{S} \mid \wildcard{Y}{\type{P}}{\bot} \in \Delta} \\ % \ol{M'} &= \set{ \generics{\ol{Y} \triangleleft \ol{S}}\ \sigma(\tv{a}) \ \texttt{m}(\ol{\sigma(\tv{a})\ x}) = \texttt{e} \mid (\mathtt{m}(\ol{x})\ = \mv e) \in \ol{M}, (\exptype{C}{\ol{X} \triangleleft \ol{N}}.\mv{m} : \ol{\tv{a}} \to \tv{a}) \in \overline{\methodAssumption}} % %TODO: Describe whole algorithm (Insert types, try out every unify solution by backtracking (describe it as Non Deterministic algorithm)) % \end{array}\\ % \textbf{in}\ \texttt{class}\ \exptype{C}{\ol{X} % \triangleleft \ol{N}} \triangleleft \type{N}\ \{ \overline{\type{T} \ f};\ \ol{M'} \} \\ % \textbf{in}\ \mtypeEnvironment \cup % \set{(\exptype{C}{\ol{X} \triangleleft \ol{N}}.\mv{m} : \generics{\ol{Y} \triangleleft \ol{S}}\ \ol{\sigma(\tv{a})} \to \sigma(\tv{a})) \ |\ (\exptype{C}{\ol{X} \triangleleft \ol{N}}.\mv{m} : \ol{\tv{a}} \to \tv{a}) \in \overline{\methodAssumption}} % % \fjtypeInsert(\overline{\methodAssumption}, (\sigma, \unifyGenerics{}) ) % \end{gather*} % The overall algorithm is nondeterministic. The function $\unify{}$ may % return finitely many times as there may be multiple solutions for a constraint % set. A local solution for class $\mv C$ may not % be compatible with the constraints generated for a subsequent class. In this case, we have to backtrack to $\mv C$ and proceed to the next % local solution; if thats fail we have to backtrack further to an earlier class. % \begin{gather*} % \textbf{ApplyTypes}(\mtypeEnvironment, \texttt{class}\ \exptype{C}{\ol{X} % \triangleleft \ol{N}} \triangleleft \type{N}\ \{ \overline{\type{T} \ f};\ \ol{M} \}) = \\ % \quad \quad \begin{array}[t]{rl} % \textbf{let}\ % \ol{M'} &= \set{ \generics{\ol{Y} \triangleleft \ol{S}}\ \sigma(\tv{a}) \ \texttt{m}(\ol{\sigma(\tv{a})\ x}) = \texttt{e} \mid (\mathtt{m}(\ol{x})\ = \mv e) \in \ol{M}, (\exptype{C}{\ol{X} \triangleleft \ol{N}}.\mv{m} : \ol{\tv{a}} \to \tv{a}) \in \overline{\methodAssumption}} % \end{array}\\ % \textbf{in}\ \texttt{class}\ \exptype{C}{\ol{X} % \triangleleft \ol{N}} \triangleleft \type{N}\ \{ \overline{\type{T} \ f};\ \ol{M'} \} \\ % \end{gather*} % %TODO: Rules to create let statements % % Input is type solution and untyped program. % % Output is typed program % % describe conversion for each expression % Given a result $(\Delta, \sigma)$ and the type placeholders generated by $\TYPE{}$ % we can construct a \wildFJ{} program. % %TODO: show soundness by comparing constraints and type rules % % untyped expression | constraints | typed expression (making use of constraints and sigma) % $\begin{array}{l|c|r} % m(x) = e & r m(p x) = e & \Delta \sigma(r) m(\sigma(p) x) = |e| \\ % e \elvis{} e' \\ % e.m(\ol{e}) & (e:a).m(\ol{e:p}) & a <. T, p <. T & let x : sigma(a) = e in e.m(x); %TODO % \end{array}$ % \begin{displaymath} % \begin{array}[c]{l} % \\ % \hline % \vspace*{-0.4cm}\\ % \wildcardEnv % \vdash C \cup \, \set{ % \ol{\type{S}} \doteq [\ol{\wtv{a}}/\overline{\rwildcard{A}}]\ol{\type{T}}, % \ol{\wtv{a}} \lessdot [\ol{\wtv{a}}/\overline{\rwildcard{A}}]\ol{U}, [\ol{\wtv{a}}/\overline{\rwildcard{A}}]\ol{L} \lessdot \ol{\wtv{a}} } % \end{array} % \end{displaymath}