Retrofitting null-safety onto Java at Meta
- We developed a brand new static evaluation device known as Nullsafe that’s used at Meta to detect NullPointerException (NPE) errors in Java code.
- Interoperability with legacy code and gradual deployment mannequin have been key to Nullsafe’s extensive adoption and allowed us to recuperate some null-safety properties within the context of an in any other case null-unsafe language in a multimillion-line codebase.
- Nullsafe has helped considerably scale back the general variety of NPE errors and improved builders’ productiveness. This exhibits the worth of static evaluation in fixing real-world issues at scale.
Null dereferencing is a standard kind of programming error in Java. On Android, NullPointerException (NPE) errors are the largest cause of app crashes on Google Play. Since Java doesn’t present instruments to specific and test nullness invariants, builders need to depend on testing and dynamic evaluation to enhance reliability of their code. These strategies are important however have their very own limitations by way of time-to-signal and protection.
In 2019, we began a undertaking known as 0NPE with the objective of addressing this problem inside our apps and considerably enhancing null-safety of Java code via static evaluation.
Over the course of two years, we developed Nullsafe, a static analyzer for detecting NPE errors in Java, built-in it into the core developer workflow, and ran a large-scale code transformation to make many million traces of Java code Nullsafe-compliant.

Taking Instagram, one in every of Meta’s largest Android apps, for instance, we noticed a 27 p.c discount in manufacturing NPE crashes through the 18 months of code transformation. Furthermore, NPEs are now not a number one explanation for crashes in each alpha and beta channels, which is a direct reflection of improved developer expertise and growth velocity.
The issue of nulls
Null pointers are infamous for inflicting bugs in packages. Even in a tiny snippet of code just like the one beneath, issues can go fallacious in quite a lot of methods:
Itemizing 1: buggy getParentName technique
Path getParentName(Path path)
return path.getParent().getFileName();
- getParent() could produce null and trigger a NullPointerException regionally in getParentName(…).
- getFileName() could return null which can propagate additional and trigger a crash in another place.
The previous is comparatively simple to identify and debug, however the latter could show difficult — particularly because the codebase grows and evolves.
Determining nullness of values and recognizing potential issues is simple in toy examples just like the one above, nevertheless it turns into extraordinarily arduous on the scale of thousands and thousands of traces of code. Then including hundreds of code modifications a day makes it unimaginable to manually be sure that no single change results in a NullPointerException in another part. In consequence, customers undergo from crashes and software builders have to spend an inordinate quantity of psychological vitality monitoring nullness of values.
The issue, nonetheless, is just not the null worth itself however somewhat the shortage of specific nullness info in APIs and lack of tooling to validate that the code correctly handles nullness.
Java and nullness
In response to those challenges Java 8 launched java.util.Non-compulsory<T> class. However its efficiency influence and legacy API compatibility points meant that Non-compulsory couldn’t be used as a general-purpose substitute for nullable references.
On the identical time, annotations have been used with success as a language extension level. Specifically, including annotations resembling @Nullable and @NotNull to common nullable reference varieties is a viable strategy to prolong Java’s varieties with specific nullness whereas avoiding the downsides of Non-compulsory. Nevertheless, this method requires an exterior checker.
An annotated model of the code from Itemizing 1 may appear to be this:
Itemizing 2: appropriate and annotated getParentName technique
// (2) (1)
@Nullable Path getParentName(Path path)
Path dad or mum = path.getParent(); // (3)
return dad or mum != null ? dad or mum.getFileName() : null;
// (4)
In comparison with a null-safe however not annotated model, this code provides a single annotation on the return kind. There are a number of issues price noting right here:
- Unannotated varieties are thought-about not-nullable. This conference enormously reduces the annotation burden however is utilized solely to first-party code.
- Return kind is marked @Nullable as a result of the strategy can return null.
- Native variable dad or mum is just not annotated, as its nullness have to be inferred by the static evaluation checker. This additional reduces the annotation burden.
- Checking a worth for null refines its kind to be not-nullable within the corresponding department. That is known as flow-sensitive typing, and it permits writing code idiomatically and dealing with nullness solely the place it’s actually essential.
Code annotated for nullness will be statically checked for null-safety. The analyzer can defend the codebase from regressions and permit builders to maneuver sooner with confidence.
Kotlin and nullness
Kotlin is a contemporary programming language designed to interoperate with Java. In Kotlin, nullness is specific within the varieties, and the compiler checks that the code is dealing with nullness accurately, giving builders on the spot suggestions.
We acknowledge these benefits and, actually, use Kotlin closely at Meta. However we additionally acknowledge the very fact that there’s a lot of business-critical Java code that can’t — and typically mustn’t — be moved to Kotlin in a single day.
The 2 languages – Java and Kotlin – need to coexist, which implies there may be nonetheless a necessity for a null-safety resolution for Java.
Static evaluation for nullness checking at scale
Meta’s success constructing different static evaluation instruments resembling Infer, Hack, and Flow and making use of them to real-world code-bases made us assured that we might construct a nullness checker for Java that’s:
- Ergonomic: understands the circulation of management within the code, doesn’t require builders to bend over backward to make their code compliant, and provides minimal annotation burden.
- Scalable: in a position to scale from lots of of traces of code to thousands and thousands.
- Appropriate with Kotlin: for seamless interoperability.
On reflection, implementing the static evaluation checker itself was most likely the straightforward half. The true effort went into integrating this checker with the event infrastructure, working with the developer communities, after which making thousands and thousands of traces of manufacturing Java code null-safe.
We carried out the primary model of our nullness checker for Java as a part of Infer, and it served as an incredible basis. Afterward, we moved to a compiler-based infrastructure. Having a tighter integration with the compiler allowed us to enhance the accuracy of the evaluation and streamline the combination with growth instruments.
This second model of the analyzer is named Nullsafe, and we will probably be protecting it beneath.
Null-checking underneath the hood
Java compiler API was launched by way of JSR-199. This API provides entry to the compiler’s inside illustration of a compiled program and permits customized performance to be added at completely different levels of the compilation course of. We use this API to increase Java’s type-checking with an additional go that runs Nullsafe evaluation after which collects and reviews nullness errors.
Two primary knowledge constructions used within the evaluation are the summary syntax tree (AST) and management circulation graph (CFG). See Itemizing 3 and Figures 2 and three for examples.
- The AST represents the syntactic construction of the supply code with out superfluous particulars like punctuation. We get a program’s AST by way of the compiler API, along with the kind and annotation info.
- The CFG is a flowchart of a chunk of code: blocks of directions related with arrows representing a change in management circulation. We’re utilizing the Dataflow library to construct a CFG for a given AST.
The evaluation itself is break up into two phases:
- The kind inference part is answerable for determining nullness of varied items of code, answering questions resembling:
- Can this technique invocation return null at program level X?
- Can this variable be null at program level Y?
- The kind checking part is answerable for validating that the code doesn’t do something unsafe, resembling dereferencing a nullable worth or passing a nullable argument the place it’s not anticipated.
Itemizing 3: instance getOrDefault technique
String getOrDefault(@Nullable String str, String defaultValue)
if (str == null) return defaultValue;
return str;


Sort-inference part
Nullsafe does kind inference primarily based on the code’s CFG. The results of the inference is a mapping from expressions to nullness-extended varieties at completely different program factors.
state = expression x program level → nullness – prolonged kind
The inference engine traverses the CFG and executes each instruction based on the evaluation’ guidelines. For a program from Itemizing 3 this may appear to be this:
- We begin with a mapping at <entry> level:
- str → @Nullable String, defaultValue → String.
- After we execute the comparability str == null, the management circulation splits and we produce two mappings:
- THEN: str → @Nullable String, defaultValue → String.
- ELSE: str → String, defaultValue → String.
- When the management circulation joins, the inference engine wants to provide a mapping that over-approximates the state in each branches. If now we have @Nullable String in a single department and String in one other, the over-approximated kind could be @Nullable String.

The principle advantage of utilizing a CFG for inference is that it permits us to make the evaluation flow-sensitive, which is essential for an evaluation like this to be helpful in apply.
The instance above demonstrates a quite common case the place nullness of a worth is refined based on the management circulation. To accommodate real-world coding patterns, Nullsafe has assist for extra superior options, starting from contracts and complicated invariants the place we use SAT fixing to interprocedural object initialization evaluation. Dialogue of those options, nonetheless, is outdoors the scope of this submit.
Sort-checking part
Nullsafe does kind checking primarily based on this system’s AST. By traversing the AST, we are able to evaluate the knowledge specified within the supply code with the outcomes from the inference step.
In our instance from Itemizing 3, once we go to the return str node we fetch the inferred kind of str expression, which occurs to be String, and test whether or not this sort is suitable with the return kind of the strategy, which is said as String.

After we see an AST node similar to an object dereference, we test that the inferred kind of the receiver excludes null. Implicit unboxing is handled in an identical manner. For technique name nodes, we test that the inferred sorts of the arguments are suitable with technique’s declared varieties. And so forth.
Total, the type-checking part is far more easy than the type-inference part. One nontrivial side right here is error rendering, the place we have to increase a kind error with a context, resembling a kind hint, code origin, and potential fast repair.
Challenges in supporting generics
Examples of the nullness evaluation given above lined solely the so-called root nullness, or nullness of a worth itself. Generics add an entire new dimension of expressivity to the language and, equally, nullness evaluation will be prolonged to assist generic and parameterized courses to additional enhance the expressivity and precision of APIs.
Supporting generics is clearly factor. However further expressivity comes as a price. Specifically, kind inference will get much more sophisticated.
Contemplate a parameterized class Map<Ok, Checklist<Pair<V1, V2>>>. Within the case of non-generic nullness checker, there may be solely the foundation nullness to deduce:
// NON-GENERIC CASE
␣ Map<Ok, Checklist<Pair<V1, V2>>
// ^
// --- Solely the foundation nullness must be inferred
The generic case requires much more gaps to fill on high of an already complicated flow-sensitive evaluation:
// GENERIC CASE
␣ Map<␣ Ok, ␣ Checklist<␣ Pair<␣ V1, ␣ V2>>
// ^ ^ ^ ^ ^ ^
// -----|----|------|------|------|--- All these must be inferred
This isn’t all. Generic varieties that the evaluation infers should carefully observe the form of the kinds that Java itself inferred to keep away from bogus errors. For instance, contemplate the next snippet of code:
interface Animal
class Cat implements Animal
class Canine implements Animal
void targetType(@Nullable Cat catMaybe)
Checklist<@Nullable Animal> animalsMaybe = Checklist.of(catMaybe);
Checklist.<T>of(T…) is a generic technique and in isolation the kind of Checklist.of(catMaybe) could possibly be inferred as Checklist<@Nullable Cat>. This may be problematic as a result of generics in Java are invariant, which signifies that Checklist<Animal> is just not suitable with Checklist<Cat> and the project would produce an error.
The explanation this code kind checks is that the Java compiler is aware of the kind of the goal of the project and makes use of this info to tune how the kind inference engine works within the context of the project (or a way argument for the matter). This characteristic is named goal typing, and though it improves the ergonomics of working with generics, it doesn’t play properly with the type of ahead CFG-based evaluation we described earlier than, and it required further care to deal with.
Along with the above, the Java compiler itself has bugs (e.g., this) that require varied workarounds in Nullsafe and in different static evaluation instruments that work with kind annotations.
Regardless of these challenges, we see important worth in supporting generics. Specifically:
- Improved ergonomics. With out assist for generics, builders can’t outline and use sure APIs in a null-aware manner: from collections and useful interfaces to streams. They’re compelled to bypass the nullness checker, which harms reliability and reinforces a foul behavior. We’ve discovered many locations within the codebase the place lack of null-safe generics led to brittle code and bugs.
- Safer Kotlin interoperability. Meta is a heavy person of Kotlin, and a nullness evaluation that helps generics closes the hole between the 2 languages and considerably improves the protection of the interop and the event expertise in a heterogeneous codebase.
Coping with legacy and third-party code
Conceptually, the static evaluation carried out by Nullsafe provides a brand new set of semantic guidelines to Java in an try and retrofit null-safety onto an in any other case null-unsafe language. The perfect state of affairs is that every one code follows these guidelines, through which case diagnostics raised by the analyzer are related and actionable. The fact is that there’s a variety of null-safe code that is aware of nothing in regards to the new guidelines, and there’s much more null-unsafe code. Operating the evaluation on such legacy code and even newer code that calls into legacy parts would produce an excessive amount of noise, which might add friction and undermine the worth of the analyzer.
To cope with this drawback in Nullsafe, we separate code into three tiers:
- Tier 1: Nullsafe compliant code. This contains first-party code marked as @Nullsafe and checked to don’t have any errors. This additionally contains identified good annotated third-party code or third-party code for which now we have added nullness fashions.
- Tier 2: First-party code not compliant with Nullsafe. That is inside code written with out specific nullness monitoring in thoughts. This code is checked optimistically by Nullsafe.
- Tier 3: Unvetted third-party code. That is third-party code that Nullsafe is aware of nothing about. When utilizing such code, the makes use of are checked pessimistically and builders are urged so as to add correct nullness fashions.
The vital side of this tiered system is that when Nullsafe type-checks Tier X code that calls into Tier Y code, it makes use of Tier Y’s guidelines. Specifically:
- Calls from Tier 1 to Tier 2 are checked optimistically,
- Calls from Tier 1 to Tier 3 are checked pessimistically,
- Calls from Tier 2 to Tier 1 are checked based on Tier 1 part’s nullness.
Two issues are price noting right here:
- In line with level A, Tier 1 code can have unsafe dependencies or protected dependencies used unsafely. This unsoundness is the worth we needed to pay to streamline and gradualize the rollout and adoption of Nullsafe within the codebase. We tried different approaches, however further friction rendered them extraordinarily arduous to scale. The excellent news is that as extra Tier 2 code is migrated to Tier 1 code, this level turns into much less of a priority.
- Pessimistic remedy of third-party code (level B) provides further friction to the nullness checker adoption. However in our expertise, the fee was not prohibitive, whereas the advance within the security of Tier 1 and Tier 3 code interoperability was actual.

Deployment, automation, and adoption
A nullness checker alone is just not sufficient to make an actual influence. The impact of the checker is proportional to the quantity of code compliant with this checker. Thus a migration technique, developer adoption, and safety from regressions change into main issues.
We discovered three details to be important to our initiative’s success:
- Fast fixes are extremely useful. The codebase is stuffed with trivial null-safety violations. Instructing a static evaluation to not solely test for errors but in addition to give you fast fixes can cowl a variety of floor and provides builders the area to work on significant fixes.
- Developer adoption is vital. Which means the checker and associated tooling ought to combine effectively with the primary growth instruments: construct instruments, IDEs, CLIs, and CI. However extra vital, there must be a working suggestions loop between software and static evaluation builders.
- Knowledge and metrics are vital to maintain the momentum. Figuring out the place you might be, the progress you’ve made, and the subsequent neatest thing to repair actually helps facilitate the migration.
Longer-term reliability influence
As one instance, 18 months of reliability knowledge for the Instagram Android app:
- The portion of the app’s code compliant with Nullsafe grew from 3 p.c to 90 p.c.
- There was a major lower within the relative quantity of NullPointerException (NPE) errors throughout all launch channels (see Determine 7). Significantly, in manufacturing, the amount of NPEs was decreased by 27 p.c.
This knowledge is validated in opposition to different sorts of crashes and exhibits an actual enchancment in reliability and null-safety of the app.
On the identical time, particular person product groups additionally reported important discount within the quantity of NPE crashes after addressing nullness errors reported by Nullsafe.
The drop in manufacturing NPEs diverse from staff to staff, with enhancements ranging from 35 p.c to 80 p.c.
One significantly fascinating side of the outcomes is the drastic drop in NPEs within the alpha-channel. This immediately displays the advance within the developer productiveness that comes from utilizing and counting on a nullness checker.
Our north star objective, and a great state of affairs, could be to utterly remove NPEs. Nevertheless, real-world reliability is complicated, and there are extra components enjoying a task:
- There may be nonetheless null-unsafe code that’s, actually, answerable for a big proportion of high NPE crashes. However now we’re ready the place focused null-safety enhancements could make a major and lasting influence.
- The amount of crashes is just not one of the best metric to measure reliability enchancment as a result of one bug that slips into manufacturing can change into highly regarded and single-handedly skew the outcomes. A greater metric is likely to be the variety of new distinctive crashes per launch, the place we see n-fold enchancment.
- Not all NPE crashes are attributable to bugs within the app’s code alone. A mismatch between the shopper and the server is one other main supply of manufacturing points that must be addressed by way of different means.
- The static evaluation itself has limitations and unsound assumptions that allow sure bugs slip into manufacturing.
It is very important word that that is the mixture impact of lots of of engineers utilizing Nullsafe to enhance the protection of their code in addition to the impact of different reliability initiatives, so we are able to’t attribute the advance solely to the usage of Nullsafe. Nevertheless, primarily based on reviews and our personal observations over the course of the previous few years, we’re assured that Nullsafe performed a major function in driving down NPE-related crashes.

Past Meta
The issues outlined above are hardly particular to Meta. Sudden null-dereferences have brought about countless problems in different companies. Languages like C# developed into having explicit nullness of their kind system, whereas others, like Kotlin, had it from the very starting.
In terms of Java, there have been a number of makes an attempt so as to add nullness, beginning with JSR-305, however none was broadly profitable. At present, there are numerous nice static evaluation instruments for Java that may test nullness, together with CheckerFramework, SpotBugs, ErrorProne, and NullAway, to call just a few. Specifically, Uber walked the same path by making their Android codebase null-safe utilizing NullAway checker. However ultimately, all of the checkers carry out nullness evaluation in several and subtly incompatible methods. The shortage of normal annotations with exact semantics has constrained the usage of static evaluation for Java all through the trade.
This drawback is precisely what the JSpecify workgroup goals to handle. The JSpecify began in 2019 and is a collaboration between people representing firms resembling Google, JetBrains, Uber, Oracle, and others. Meta has additionally been a part of JSpecify since late 2019.
Though the standard for nullness is just not but finalized, there was a variety of progress on the specification itself and on the tooling, with extra thrilling bulletins following quickly. Participation in JSpecify has additionally influenced how we at Meta take into consideration nullness for Java and about our personal codebase evolution.