Contents:
NoSuchElementExceptionThe Checker Framework is an innovative programming tool that prevents bugs at development time, before they escape to production.
Java's type system prevents some bugs, such as int count =
"hello";. However, it does not prevent other bugs, such as null
pointer dereferences, concurrency errors, disclosure of private
information, incorrect internationalization, out-of-bounds indices, etc.
Pluggable type-checking replaces a
programming language's built-in type system with a more powerful,
expressive one.
We have created over 20 new type systems, and other people have created over 30 more. A type system is not just a bug-finding tool: it is a verification tool that gives a guarantee that no errors (of certain types) exist in your program. Even though it is powerful, it is easy to use. It follows the standard typing rules that programmers already know, and it fits into their workflow.
The Checker Framework is popular: it is used daily at Amazon, Google, Meta, Oracle, Uber, on Wall Street, and in other companies from big to small. It is attractive to programmers who care about their craft and the quality of their code. The Checker Framework is the motivation for Java's type annotations feature. It has received multiple awards. With this widespread use, there is a need for people to help with the project: everything from bug fixes, to new features, to case studies, to integration with other tools. We welcome your contribution!
Why should you join this project? It's popular, so you will have an impact. It makes code more robust and secure, which is a socially important purpose. You will get to scratch your own itch by creating tools that solve problems that frustrate you. It is accessible even to junior software engineers and undergraduates. (Many undergraduate students have published scientific papers, such as Jason Waataja, Vlastimil Dort Gene Kim, Siwakorn Srisakaokul, Stephanie Dietzel, Nathaniel Mote, Brian Walker, Eric Spishak, Jaime Quinonez, Matthew Papi, Mahmood Ali, and Telmo Correa; and even more have made significant contributions to the tool.) Finally, we have a lot of fun on this project!
Prerequisites: You should be very comfortable with the Java programming language and its type system. You should know how a type system helps you and how it can hinder you. You should be willing to dive into a moderately-sized codebase. You should understand fundamental object-oriented programming concepts, such as behavioral subtyping: subtyping theory permits argument types to change contravariantly (even though Java forbids it for reasons related to overloading), whereas return types may change covariantly both in theory and in Java.
Potential projects: Most of this document lists potential projects. The projects are grouped roughly from easiest to most challenging.
To get started, first do a case study of using the Checker Framework: that is, run the Checker Framework on some program. If you have already done so, you can skip this section. Otherwise, a case study gives you experience in using the Checker Framework, and it may reveal bugs in either the Checker Framework or in the program it is analyzing.
Why should you start with a case study? Before you can contribute to any project, you must understand the tool from a user point of view, including its strengths, weaknesses, and how to use it. Using the Checker Framework is the best way to learn about it and determine whether you would enjoy contributing to it.
What is the purpose of a case study?
The primary result of your case study is that you will discover bugs in the
subject program, or you will verify that it has no bugs (of some particular
type). If you find bugs in open-source code,
and let us know when they are resolved.
Another outcome of your case study is that you may discover bugs, limitations,
or usability problems in the Checker Framework. Please
report them.
We'll try to fix them, or they might give you inspiration for
improvements you would like to make to the Checker Framework.
You might want to start with a small program that you wrote, then repeat the process with a larger open-source program or library.
import statements. Doing so bloats the size of the
diffs and makes it hard to understand the essential changes.
if statement that always succeeds, just
to suppress a warning. Convince yourself that both branches can
execute, or else don't add the if statement.
@SuppressWarnings annotation,
write
it on the smallest possible scope and
explain
why the checker warning is a false positive and you are certain
the code is safe.
The subject line should be descriptive (not just "Case study", but "Nullness case study of Apache Commons Exec library"). You should give us access to
You can also try to fix problems that you find and submit a pull request, but that is not a requirement to get started, because not all problems are good for new contributors.
We are very happy to answer your questions, and we are eager to interact with you! It's OK to have questions, and your questions can lead to improvements in the documentation and the tool.
Before you ask a question, read this file and the "Troubleshooting" section of the Checker Framework manual (including "How to report problems"), and also search in the Checker Framework manual for the answer. Don't send us a message that says nothing but “please guide me” or “tell me how to fix this issue from the issue tracker”.
When you ask a question, please tell us what you have tried, tell us what went wrong or where you got stuck, and ask a concrete technical question that will help you get past your problem. If you can do that, then definitely ask your question, because we don't want you to be stuck or frustrated.
When you send email, please use standard email etiquette, such as: avoid all-caps; use a descriptive subject line; don't put multiple different topics in a single email message; start a new thread with a new subject line when you change the topic; don't clutter discussions with irrelevant remarks; don't use screenshots (unless there is a problem with a GUI), but instead cut-and-paste the output or code into your message; if you are making a guess, clearly indicate that it is a guess and your grounds for it. Bug reports should be complete and should usually be reported to the issue tracker.
Here are some possible focuses for a project:
This document gives a few suggestions in each category.
These projects evaluate a recently-written type system or a feature used by multiple type systems. Using the type systems on real code is our most important source of new ideas and improvements. Many people have started out “just” doing a case study but have ended up making deep, fundamental contributions and even publishing scientific papers about their discoveries.
One possible outcome is to identify weaknesses in the type-checker so that we can improve it. Another possible outcome is to provide evidence that the type-checker is effective and convince more users to adopt it. You will probably also discover defects (bugs) in the codebase being type-checked.
Determine whether the ASM library, or some other library, properly handles signature strings.
Some challenging aspects of this case study are:
An unsigned integer's bits are interpreted differently than a signed integer's bits. It is meaningless to add a signed and an unsigned integer — the result will be nonsense bits. The same is true of printing and of other numeric operators such as multiplication and comparison.
We have a prototype compile-time verification tool that detects and prevents these errors. The goal of this project is to perform case studies to determine how often programmers make signedness errors (our initial investigation suggests that this is common!) and to improve the verification tool.
The research questions are:
The methodology is:
A good way to find projects that use unsigned arithmetic is to find a library that supports unsigned arithmetic, then search on GitHub for projects that use that library.
Here are some relevant libraries.
Integer
and Long, these include
compareUnsigned,
divideUnsigned,
parseUnsignedInt,
remainderUnsigned, and
toUnsignedLong.
DataInputStream, ObjectInputStream,
and RandomAccessFile have readUnsignedByte.
Arrays has compareUnsigned.
The JDK is already annotated; search for @Unsigned within
https://github.com/typetools/jdk.
@Unsigned within
https://github.com/typetools/guava.
Another possibility is to find Java projects that could use an unsigned arithmetic library but do not. For example, bc-java defines its own unsigned libraries, and some other programs might do direct bit manipulation.
A type system is useful because it prevents certain errors. The downside of a type system is the effort required to write the types. Type inference is the process of writing the types for a program.
The Checker Framework includes a whole-program inference that inserts type qualifiers in the user's program. It works well on some programs, but needs more enhancements to work well on all programs.
By default, the Checker Framework is unsound in several circumstances. “Unsound” means that the Checker Framework may report no warning even though the program can misbehave at run time.
The reason that the Checker Framework is unsound is that we believe that enabling these checks would cause too many false positive warnings: warnings that the Checker Framework issues because it cannot prove that the code is safe (even though a human can see that the code is safe). Having too many false positive warnings would irritate users and lead them not to use the checker at all, or would force them to simply disable those checks.
We would like to do studies of these command-line options to see whether our concern is justified. Is it prohibitive to enable sound checking? Or can we think of enhancements that would let us turn on those checks that are currently disabled by default?
There is no need to annotate new code for this project. Just use existing annotated codebases, such as those that are type-checked as part of the Checker Framework's Azure Pipeline. In other words, you can start by enabling Azure Pipelines for your fork and then changing the default behavior in a branch. The Azure Pipelines job will show you what new warnings appear.
Many other tools exist for prevention of programming errors, such as Error Prone, NullAway, FindBugs, JLint, PMD, and IDEs such as Eclipse and IntelliJ. These tools are not as powerful as the Checker Framework (some are bug finders rather than verification tools, and some perform a shallower analysis), but they may be easier to use. Programmers who use these tools wonder, "Is it worth my time to switch to using the Checker Framework?"
The goal of this project is to perform a head-to-head comparison of as many different tools as possible. You will quantify:
This project will help programmers to choose among the different tools — it will show when a programmer should or should not use the Checker Framework. This project will also indicate how each tool should be improved.
One place to start would be with an old version of a program that is known to contain bugs. Or, start with the latest version of the program and re-introduce fixed bugs. (Either of these is more realistic than introducing artificial bugs into the program.) A possibility would be to use the Lookup program that has been used in previous case studies.
Android uses its own annotations that are similar to some in the Checker
Framework. Examples include the
Android
Studio support annotations,
including @NonNull, @IntRange, @IntDef,
and others.
The goal of this project is to implement support for these annotations.
That is probably as simple as creating aliased annotations
by calling method addAliasedTypeAnnotation()
in AnnotatedTypeFactory.
Then, do a case study to show the utility (or not) of pluggable type-checking, by comparison with how Android Studio currently checks the annotations.
These projects annotate a library, so that it is easier to type-check clients of the library. Another benefit is that this may find bugs in the library. It can also give evidence for the usefulness of pluggable type-checking, or point out ways to improve the Checker Framework.
When type-checking a method call, the Checker Framework uses the method declaration's annotations. This means that in order to type-check code that uses a library, the Checker Framework needs an annotated version of the library.
The Checker Framework comes with a few annotated libraries. Increasing this number will make the Checker Framework even more useful, and easier to use.
After you have chosen a library, fork the library's source code, adjust its build system to run the Checker Framework, and add annotations to it until the type-checker issues no warnings.
Before you get started, be sure to read How to get started annotating legacy code. More generally, read the relevant sections of the Checker Framework manual.
There are several ways to choose a library to annotate:
When annotating a library, it is important to type-check both the library and at least one client that uses it. Type-checking the client will ensure that the library annotations are accurate.
Whatever library you choose, you will need to deeply understand its source code. You will find it easier to work with a library that is well-designed and well-documented.
You should choose a library that is not already annotated. There are two exceptions to this.
Guava is already partially annotated with nullness annotations — in part by Guava's developers, and in part by the Checker Framework team. However, Guava does not yet type-check without errors. Doing so could find more errors (the Checker Framework has found nullness and indexing errors in Guava in the past) and would be a good case study to learn the limitations of the Nullness Checker.
The Checker Framework is shipped with about 20 type-checkers. Users can create a new checker of their own. However, some users don't want to go to that trouble. They would like to have more type-checkers packaged with the Checker Framework for easy use.
Each of these projects requires you to design a new type system, implement it, and perform case studies to demonstrate that it is both usable and effective in finding/preventing bugs.
The lightweight
ownership mechanism of the Resource Leak Checker is not implemented as
a type system, but it should be. That would enable writing ownership
annotations on generic type arguments, like List<@Owning
Socket>. It would also enable changing the Resource Leak Checker
so that non-@Owning formal parameters do not have
their @MustCall
annotation erased.
We have some notes on possible implementation strategies.
The Nullness Checker issues a false positive warning for this code:
import java.util.PriorityQueue;
import org.checkerframework.checker.nullness.qual.NonNull;
public class MyClass {
public static void usePriorityQueue(PriorityQueue<@NonNull Object> active) {
while (!(active.isEmpty())) {
@NonNull Object queueMinPathNode = active.peek();
}
}
}
The Checker Framework does not determine that active.peek() returns a non-null value in this context.
The contract of peek() is that it returns a non-null value if the queue is not empty and the queue contains no null values.
To handle this code precisely, the Nullness Checker needs to know, for each queue, whether it is empty. This is analogous to how the Nullness Checker tracks whether a particular value is a key in a map.
It should be handled the same way: by adding a new subchecker, called the Nonempty Checker, to the Nullness Checker. Its types are:
@UnknownNonEmpty — the queue might or might not be empty
@NonEmpty — the queue is definitely non-empty
There is a start at this type-checker in branch nonempty-checker. It:
However, it is not done. (In fact, it doesn't even compile.) For information about what needs to be done, see issue #399.
When you are done, the Nullness Checker should issue only the // :: diagnostics from checker/tests/nullness/IsEmptyPoll.java — no more and no fewer.
You can test that by running the Nullness Checker on the file, and when you are done you should delete the // @skip-test line so that the file is run as part of the Checker Framework test suite.
NoSuchElementException
A Java program that uses an Iterator can
throw NoSuchElementException if the program
calls next() on the Iterator but
the Iterator has no more elements to iterate over. Such
exceptions even occur in production code (for example,
in Eclipse's
rdf4j).
We would like a compile-time guarantee that this run-time error will never
happen. Our analysis will statically determine whether
the hasNext() method would return true. The basic type system
has two type qualifiers: @HasNext is a subtype
of @UnknownHasNext.
A variable's type is @HasNext if the program
calls hasNext() and it returns true. Implementing this is
easy (see
the dataflow
section in
the "How
to create a new checker" chapter). The analysis can also permit some
calls to next() even if the programmer has not
called hasNext(). For example, a call to next()
is permitted on a newly-constructed iterator that is made from a non-empty
collection. (This special case could build upon
the Non-Empty Checker mentioned above.)
There are probably other special cases, which experimentation will reveal.
Parts of this are already implemented, but it needs to be enhanced. Once case studies have demonstrated its effectiveness, then it can be released to the world, and a scientific paper can be written.
Many security vulnerabilities result from use of untrusted data without sanitizing it first. Examples include SQL injection, cross-site scripting, command injection, and many more. Other vulnerabilities result from leaking private data, such as credit card numbers.
We have built a generalized taint analysis that can address any of these problems. However, because it is so general, it is not very useful. A user must customize it for each particular problem.
The goal of this project is to make those customizations, and to evaluate their usefulness. A specific research question is: "To what extent is a general taint analysis useful in eliminating a wide variety of security vulnerabilities? How much customization, if any, is needed?"
The generalized taint analysis is the Checker Framework's a Tainting Checker. It requires customization to a particular domain:
@Tainted and @Untainted qualifiers
to something more specific (such as @Private
or @PaymentDetails or @HtmlQuoted), and
The first part of this project is to make this customization easier to do — preferably, a user will not have to change any code in the Checker Framework (the Subtyping Checker already works this way). As part of making customization easier, a user should be able to specify multiple levels of taint — many information classification hierarchies have more than two levels. For example, the US government separates information into four categories: Unclassified, Confidential, Secret, and Top Secret.
The second part of this project is to provide several examples, and do case studies showing the utility of compile-time taint checking.
Possible examples include:
@PrivacySource and @PrivacySink
annotations used by the Meta Infer
static analyzer.
For some microbenchmarks, see the Juliette test suite for Java from CWE.
In Java, some objects do not fully implement their interface; they
throw UnsupportedOperationException for some operations. One
example is unmodifiable collections. They throw the exception when a
mutating operation is called, such
as add, addAll, put, remove,
etc.
The goal of this project is to
design a compile-time verification tool to track which operations might not be supported.
This tool will issue a warning whenever
an UnsupportedOperationException might occur at run time.
This helps programmers to avoid run-time exceptions (crashes) in their Java programs.
The research questions include:
UnsupportedOperationException? What design is effective?
UnsupportedOperationException exceptions
pervasive in Java programs? Is it possible to eliminate them?
The methodology is:
Here is a possible design, as a pluggable type system.
@Unmodifiable
|
@Modifiable
In other words, the @Unmodifiable type qualifier is a
supertype of @Modifiable. This means that a @Modifiable
List can be used where an @Unmodifiable List is
expected, but not vice versa.
@Modifable is the default, and
methods such
as Arrays.asList
and Collections.emptyList
must be annotated to return the less-capable supertype.
Overflow is when 32-bit arithmetic differs from ideal arithmetic. For
example, in Java the int computation 2,147,483,647 + 1 yields
a negative number, -2,147,483,648. The goal of this project is to detect
and prevent problems such as these.
One way to write this is as an extension of the Constant Value Checker, which already keeps track of integer ranges. It even already checks for overflow, but it never issues a warning when it discovers possible overflow. Your variant would do so.
This problem is so challenging that there has been almost no previous
research on static approaches to the problem. (Two relevant papers are
IntScope:
Automatically Detecting Integer Overflow Vulnerability in x86 Binary Using
Symbolic Execution and
Integer Overflow
Vulnerabilities Detection in Software Binary Code.) Researchers are
concerned that users will have to write a lot of annotations indicating the
possible ranges of variables, and that even so there will be a lot of false
positive warnings due to approximations in the conservative analysis.
For example, will every loop that contains i++ cause a warning that i might overflow?
That would not be acceptable: users would just disable the check.
You can convince yourself of the difficulty by manually analyzing programs to see how clever the analysis has to be, or manually simulating your proposed analysis on a selection of real-world code to learn its weaknesses. You might also try it on good and bad binary search code.
One way to make the problem tractable is to limit its scope: instead of
being concerned with all possible arithmetic overflow, focus on a specific
use case.
As one concrete application,
the Index
Checker is currently unsound in the presence of integer overflow. If
an integer i is known to be @Positive, and 1 is
added to it, then the Index Checker believes that its type
remains @Positive. If i was
already Integer.MAX_VALUE, then the result is negative —
that is, the Index Checker's approximation to it is unsound.
This project involves removing this unsoundness by implementing a type system to track when an
integer value might overflow — but this only matters for values that
are used as an array index.
That is, checking can be restricted to computations that involve an operand
of type @IntRange).
Implementing such an analysis would permit the Index Checker
to extend its guarantees even to programs that might overflow.
This analysis is important for some indexing bugs in practice.
Using the Index Checker, we found 5 bugs in Google
Guava related to overflow. Google marked these as high priority and
fixed them immediately. In practice, there would be a run-time exception
only for an array of size approximately Integer.MAX_INT.
You could write an extension of the Constant Value Checker, which already keeps track of integer ranges and even determines when overflow is possible. It doesn't issue a warning, but your checker could record whether overflow was possible (this could be a two-element type system) and then issue a warning, if the value is used as an array index. Other implementation strategies may be possible.
Here are some ideas for how to avoid the specific problem
of issuing a warning about potential overflow for every i++ in
a loop (but maybe other approaches are possible):
i == Integer.MAX_VALUE before
incrementing. This wide-scale, disruptive code change is not
acceptable.
@ArrayLenRange(0, Integer.MAX_VALUE-1) rather
than @UnknownVal, which is equivalent
to @ArrayLenRange(0, Integer.MAX_VALUE-1). Now, every
array construction requires the client to establish that the length is
not Integer.MAX_VALUE. I don't have a feel for whether
this would be unduly burdensome to users.
The Index
Checker is currently restricted to fixed-size data structures. A
fixed-size data structure is one whose length cannot be changed once it is
created, such as arrays and Strings. This limitation prevents the Index
Checker from verifying indexing operations on mutable-size data structures,
like Lists, that have add or remove
methods. Since these kind of collections are common in practice, this is a
severe limitation for the Index Checker.
The limitation is caused by the Index Checker's use of types that are dependent on the length of data structures,
like @LTLengthOf("data_structure"). If data_structure's length could change,
then the correctness of this type might change.
A naive solution would be to invalidate these types any time a method is called on data_structure.
Unfortunately, aliasing makes this still unsound. Even more, a great solution to this problem would keep
the information in the type when a method like add or remove is called on data_structure.
A more complete solution might involve some special annotations on List that permit the information to be persisted.
Another approach would be to run a pointer analysis before type-checking,
then use that information for precise information about what lists might be
changed by each call to add or remove. One
possible pointer analysis would be that
of Doop.
This project would involve designing and implementing a solution to this problem.
Verifying a program to be free of errors can be a daunting task. When starting out, a user may be more interested in bug-finding than verification. The goal of this project is to create a nullness bug detector that uses the powerful analysis of the Checker Framework and its Nullness Checker, but omits some of its more confusing or expensive features. The goal is to create a fast, easy-to-use bug detector. It would enable users to start small and advance to full verification in the future, rather than having to start out doing full verification.
This could be structured as a new NullnessLight Checker, or as a command-line argument to the current Nullness Checker. Here are some differences from the real Nullness checker:
Map.get, the given key appears in the map.@Pure: it returns
the same value on every call.@NonNull.Each of these behaviors should be controlled by its own command-line argument, as well as being enabled in the NullnessLight Checker.
The implementation may be relatively straightforward, since in most cases the behavior is just to disable some functionality of existing checkers.
Tools such as FindBugs, NullAway, NullnessLight, and the Nullness Checker form a spectrum from easy-to-use bug detectors to sound verification. NullnessLight represents a new point in the design space. It will be interesting to compare these checkers:
Uber's NullAway tool is also
an implementation of this idea (that is, a fast, but incomplete and
unsound, nullness checker). NullAway doesn't let the user specify Java
Generics: it assumes that every type parameter is @NonNull.
Does Uber's tool provide users a good
introduction to the ideas that a user can use to transition to a nullness
type system later?
Sometimes, the best way to avoid a checker warning is to use an annotated library method. Consider this code:
@FqBinaryName String fqBinaryName = ...;
@ClassGetName String componentType = fqBinaryName.substring(0, fqBinaryName.indexOf('['));
The Signature
String Checker issues a warning, because it does not reason about
arbitrary string manipulations. The code is correct, but it is in bad
style. It is confusing to perform string manipulations to convert between
different string representations. It is clearer and less error-prone (the
above code is buggy when fqBinaryName is not an array type!)
to use a library method, and the checker accepts this code because the
library method is appropriately annotated:
import org.plumelib.reflection.Signatures; ... @ClassGetName String componentType = Signatures.getArrayElementType(fqBinaryName);
However, users may not know about the library method. Therefore, the
Checker Framework should issue a warning message, along with the error
message, notifying users of the library method. For example, the Signature
String Checker would heuristically mention
the Signatures.getArrayElementType()
method when it issues an error about string manipulation where some input
is
a FqBinaryName
and the output is annotated
as ClassGetName.
It would behave similarly for other library methods.
Compiler writers have come to realize that clarity of error messages is as important as the speed of the executable (1, 2, 3, 4). This is especially true when the language or type system has rich features.
The goal of this project is to improve a compiler's error messages. Here are some distinct challenges:
@IndexFor("a") annotation is syntactic sugar for
@NonNegative @LTLengthOf("a"), and those types are
the ones that currently appear in error messages.
It might be good to show simpler types or ones that the user wrote.
A number of type annotations take, as an
argument, a
Java expression. The representation for these
(the JavaExpression
class) is a hack. The goal of this
project is to remove it.
The JavaExpression class
represents an AST. There is no need for the Checker Framework to
define its own AST when the JavaParser AST already exists and is
maintained. In fact, JavaExpressionParseUtil uses JavaParser,
but needlessly converts a
JavaParser Expression into
a JavaExpression.
The goals for the project include:
JavaExpression
by a use of the JavaParser
class com.github.javaparser.ast.expr.Expression.
JavaExpression (listed in the
"Direct Known Subclasses" section of
the JavaExpression
API documentation) by a use of a
subclass of Expression. For example, replace every use
of MethodCall by MethodCallExpr.
JavaExpressionParseUtil
class already uses JavaParser, but it uses ExpressionToReceiverVisitor to construct
a JavaExpression. Have it return a
JavaParser Expression instead, and delete ExpressionToReceiverVisitor.
Direct replacement of the classes is not possible, or we would have done it
already. For example, JavaExpression contains some methods that
JavaParser lacks, such as isUnassignableByOtherCode. As a
first step before doing the tasks listed above, you may want to convert
these methods from instance methods of JavaExpression into static
methods in JavaExpressions, making JavaExpression more
like a standard AST that can be replaced by JavaParser classes.
You also need to decide how to store the type field
of JavaExpression, when JavaExpression is eliminated.
An
alternate design (or a partial step in the refactoring process) would be to
retain the JavaExpression class, but make it a thin wrapper around
JavaParser classes that do most of the real work.
Another aspect of this project is fixing the issues that are labeled "JavaExpression".
The Checker Framework's dataflow framework (manual here) implements flow-sensitive type refinement (local type inference) and other features. It is used in the Checker Framework and also in Error Prone, NullAway, and elsewhere.
There are a number of open issues — both bugs and feature requests — related to the dataflow framework. The goal of this project is to address as many of those issues as possible, which will directly improve all the tools that use it.
A side effect analysis reports what side effects a procedure may perform, such as what variable values it may modify. A side effect analysis is essential to other program analyses. A program analysis technique makes estimates about the current values of expressions. When a method call occurs, the analysis has to throw away most of its estimates, because the method call might change any variable. However, if the method is known to have no side effects, then the analysis doesn't need to throw away its estimates, and the analysis is more precise. Thus, an improvement to the foundational side effect analysis can improve many other program analyses.
The goal of this project is to evaluate existing side effect analysis algorithms and implementations, in order to determine what is most effective and to improve them. The research questions include:
The methodology is to collect existing side effect analysis tools (two examples are Soot and Geffken); run them on open-source projects; examine the result; and then improve them.
Currently, type annotations are only displayed in Javadoc if they are explicitly written by the programmer. However, the Checker Framework provides flexible defaulting mechanisms, reducing the annotation overhead. This project will integrate the Checker Framework defaulting phase with Javadoc, showing the signatures after applying defaulting rules.
There are other type-annotation-related improvements to Javadoc that can be explored, e.g. using JavaScript to show or hide only the type annotations currently of interest.
This section is relevant only to Google Summer of Code Students.
To apply, you will submit a single PDF through the Google Summer of Code website. This PDF should contain two main parts. We suggest that you number the parts and subparts to ensure that you don't forget anything, and to ensure that we don't overlook anything when reading your application. You might find it easiest to create multiple PDFs for the different parts, then concatenate them before uploading to the website, but how you create your proposal is entirely up to you.
The proposal should have a descriptive title, both in the PDF and in the GSoC submission system. Don't use a title like "Checker Proposal" or "Proposal for GSoC". Don't distract from content with gratuitous graphics.
List the tasks or subparts that are required to complete your project. This will help you discover a part that you had forgotten. We do not require a detailed timeline, because you don't yet know enough to create one.
If you want to do a case study, say what program you will do your case study on.
If you want to create a new type system (whether one proposed on this webpage or one of your own devising), then your proposal should include the type system's user manual. You don't have to integrate it in the Checker Framework repository (in other words, use any word processor or text editor you want to create a PDF file you will submit), but you should describe your proposed checker's parts in precise English or simple formalisms, and you should follow the suggested structure.
If you want to do exactly what is already listed on this page, then just say that (but be specific about which one!), and it will not hurt your chances of being selected. However, show us what progress you have made so far. You might also give specific ideas about extensions, about details that are not mentioned on this webpage, about implementation strategies, and so forth.
Never literally cut-and-paste text that was not written by you, because that would be plagiarism. If you quote from text written by someone else, give proper credit. Don't submit a proposal that is just a rearrangement of text that already appears on this page or in the Checker Framework manual, because it does not help us to assess your likelihood of being successful.
.zip file or
provide a GitHub URL.
You should have shared the case study as soon as you finished it or as soon as you had a question that is not answered in the manual; don't wait until you submit your proposal, because that does not give us a chance to help you with feedback.
The best way to impress us is by doing a thoughtful job in the case study. The case study is even more important than the proposal text, because it shows us your abilities. The case study may result in you submitting issues against the issue tracker of the program you are annotating or of the Checker Framework. Pull requests against our GitHub project are a plus but are not required: good submitted bugs are just as valuable as bug fixes! You can also make a good impression by correctly answering questions from other students on the GSoC mailing list.
Some GSoC projects have a requirement to fix an issue in the issue tracker. We do not, because it is unproductive. Don't try to start fixing issues before you understand the Checker Framework from the user point of view, which will not happen until you have completed a case study on an open-source program. You may discuss your ideas with us by sending mail to checker-framework-gsoc@googlegroups.com.
Get feedback! Feel free to ask questions to make your application more competitive. We want you to succeed. Historically, students who start early and get feedback are most successful. You can submit a draft proposal via the Google Summer of Code website, and we will review it. We do not receive any notification when you submit a draft proposal, so if you want feedback, please tell us that. Also, we can only see draft proposals; we cannot see final proposals until after the application deadline has passed.
Please do not violate the guidelines in the How to get help and ask questions section of this document. If you do so, you are disqualified you from participating in GSoC, because it shows that you do not read instructions, and you haven't thought about the problem nor tried to solve it.