Runtime verification is a computing system analysis and execution approach based on extracting information from a running system and using it to detect and possibly react to observed behaviors satisfying or violating certain properties.[1] Some very particular properties, such as datarace and deadlock freedom, are typically desired to be satisfied by all systems and may be best implemented algorithmically. Other properties can be more conveniently captured as formal specifications. Runtime verification specifications are typically expressed in trace predicate formalisms, such as finite state machines, regular expressions, context-free patterns, linear temporal logics, etc., or extensions of these. This allows for a less ad-hoc approach than normal testing. However, any mechanism for monitoring an executing system is considered runtime verification, including verifying against test oracles and reference implementations . When formal requirements specifications are provided, monitors are synthesized from them and infused within the system by means of instrumentation. Runtime verification can be used for many purposes, such as security or safety policy monitoring, debugging, testing, verification, validation, profiling, fault protection, behavior modification (e.g., recovery), etc. Runtime verification avoids the complexity of traditional formal verification techniques, such as model checking and theorem proving, by analyzing only one or a few execution traces and by working directly with the actual system, thus scaling up relatively well and giving more confidence in the results of the analysis (because it avoids the tedious and error-prone step of formally modelling the system), at the expense of less coverage. Moreover, through its reflective capabilities runtime verification can be made an integral part of the target system, monitoring and guiding its execution during deployment.
Checking formally or informally specified properties against executing systems or programs is an old topic (notable examples are dynamic typing in software, or fail-safe devices or watchdog timers in hardware), whose precise roots are hard to identify. The terminology runtime verification was formally introduced as the name of a 2001 workshop[2] aimed at addressing problems at the boundary between formal verification and testing. For large code bases, manually writing test cases turns out to be very time consuming. In addition, not all errors can be detected during development. Early contributions to automated verification were made at the NASA Ames Research Center by Klaus Havelund and Grigore Rosu to archive high safety standards in spacecraft, rovers and avionics technology.[3] They proposed a tool to verify specifications in temporal logic and to detect race conditions and deadlocks in Java programs by analyzing single execution paths.
Currently, runtime verification techniques are often presented with various alternative names, such as runtime monitoring, runtime checking, runtime reflection, runtime analysis, dynamic analysis, runtime/dynamic symbolic analysis, trace analysis, log file analysis, etc., all referring to instances of the same high-level concept applied either to different areas or by scholars from different communities. Runtime verification is intimately related to other well-established areas, such as testing (particularly model-based testing) when used before deployment and fault-tolerant systems when used during deployment.
Within the broad area of runtime verification, one can distinguish several categories, such as:
The broad field of runtime verification methods can be classified by three dimensions:
Nevertheless, the basic process in runtime verification remains similar:[9]
The examples below discuss some simple properties that have been considered, possibly with small variations, by several runtime verification groups by the time of this writing (April 2011). To make them more interesting, each property below uses a different specification formalism and all of them are parametric. Parametric properties are properties about traces formed with parametric events, which are events that bind data to parameters. Here a parametric property has the form
\forallparameters:\varphi
\varphi
\varphi
update in the UnsafeEnumExample) are dummy methods, which are not part of the Java API, that are used for clarity.The Java Iterator interface requires that the hasNext method be called and return true before the next method is called. If thisdoes not occur, it is very possible that a user will iterate "off the end of" a Collection. The figure to the right shows a finite state machine that defines a possible monitor for checking and enforcing this property with runtime verification. From the unknown state, it is always an error to call the next method because such an operation could be unsafe. If hasNext is called and returns true, it is safe to call next, so the monitor enters the more state. If, however, the hasNext method returns false, there are no more elements, and the monitor enters the none state. In the more and none states, calling the hasNext method provides no new information. It is safe to call the next method from the more state, but it becomes unknown if more elements exist, so the monitor reenters the initial unknown state. Finally, calling the next method from the none state results in entering the error state. What follows is a representation of this property using parametric past time linear temporal logic.
\forall~Iterator~i i.next~ → ~\odot(i.hasNext==true)
This formula says that any call to the next method must be immediately preceded by a call to hasNext method that returns true. The property here is parametric in the Iterator i. Conceptually, this means that there will be one copy of the monitor for each possible Iterator in a test program, although runtime verification systems need not implement their parametric monitors this way. The monitor for this property would be set to trigger a handler when the formula is violated (equivalently when the finite state machine enters the error state), which will occur when either next is called without first calling hasNext, or when hasNext is called before next, but returned false.
The Vector class in Java has two means for iterating over its elements. One may use the Iterator interface, as seen in the previous example, or one may use the Enumeration interface. Besides the addition of a remove method for the Iterator interface, the main difference is that Iterator is "fail fast" while Enumeration is not. What this means is that if one modifies the Vector (other than by using the Iterator remove method) when one is iterating over the Vector using an Iterator, a ConcurrentModificationException is thrown. However, when using an Enumeration this is not a case, as mentioned. This can result in non-deterministic results from a program because the Vector is left in an inconsistent state from the perspective of the Enumeration. For legacy programs that still use the Enumeration interface, one may wish to enforce that Enumerations are not used when their underlying Vector is modified. The following parametric regular pattern can be used to enforce this behavior:
∀ Vector v, Enumeration e: (e = v.elements) (e.nextElement)* v.update e.nextElement
This pattern is parametric in both the Enumeration and the Vector. Intuitively, and as above runtime verification systems need not implement their parametric monitors this way, one may think of the parametric monitor for this property as creating and keeping track of a non-parametric monitor instance for each possible pair of Vector and Enumeration. Some events may concern several monitors at the same time, such as v.update, so the runtime verification system must (again conceptually) dispatch them to all interested monitors. Here the property is specified so that it states the bad behaviors of the program. This property, then, must be monitored for the match of the pattern. The figure to the right shows Java code that matches this pattern, thus violating the property. The Vector, v, is updated after the Enumeration, e, is created, and e is then used.
The previous two examples show finite state properties, but properties used in runtime verification may be much more complex. The SafeLock property enforces the policy that the number of acquires and releases of a (reentrant) Lock class are matched within a given method call. This, of course, disallows release of Locks in methods other than the ones that acquire them, but this is very possibly a desirable goal for the tested system to achieve. Below is a specification of this property using a parametric context-free pattern:
∀ Thread t, Lock l: S→ε | S begin(t) S end(t) | S l.acquire(t) S l.release(t)
The pattern specifies balanced sequences of nested begin/end and acquire/release pairs for each Thread and Lock (
\epsilon
t1
t2
t3
l1
l2
t1
l1
t1
l2
t2
l1
t2
l2
t3
l1
t3
l2
Most of the runtime verification research addresses one or more of the topics listed below.
Observing an executing system typically incurs some runtime overhead (hardware monitors may make an exception). It is important to reduce the overhead of runtime verification tools as much as possible, particularly when the generated monitors are deployed with the system. Runtime overhead reducing techniques include:
i.next is immediately preceded on any path by a call i.hasnext that returns true (visible on the control-flow graph).One of the major practical impediments of all formal approaches is that their users are reluctant to, or don't know and don't want to learn how to read or write specifications. In some cases the specifications are implicit, such as those for deadlocks and data-races, but in most cases they need to be produced. An additional inconvenience, particularly in the context of runtime verification, is that many existing specification languages are not expressive enough to capture the intended properties.
The capability of a runtime verifier to detect errors strictly depends on its capability to analyze execution traces. When the monitors are deployed with the system, instrumentation is typically minimal and the execution traces are as simple as possible to keep the runtime overhead low. When runtime verification is used for testing, one can afford more comprehensive instrumentations that augment events with important system information that can be used by the monitors to construct and therefore analyze more refined models of the executing system. For example, augmenting events with Vector clock information and with data and control flow information allows the monitors to construct a causal model of the running system in which the observed execution was only one possible instance. Any other permutation of events that is consistent with the model is a feasible execution of the system, which could happen under a different thread interleaving. Detecting property violations in such inferred executions (by monitoring them) makes the monitor predict errors that did not happen in the observed execution, but which can happen in another execution of the same system. An important research challenge is to extract models from execution traces that comprise as many other execution traces as possible.
Unlike testing or exhaustive verification, runtime verification holds the promise to allow the system to recover from detected violations, through reconfiguration, micro-resets, or through finer intervention mechanisms sometimes referred to as tuning or steering. Implementation of these techniques within the rigorous framework of runtime verification gives rise to additional challenges.
Researchers in Runtime Verification recognized the potential for using Aspect-oriented Programming as a technique for defining program instrumentation in a modular way. Aspect-oriented programming (AOP) generally promotes the modularization of crosscutting concerns. Runtime Verification naturally is one such concern and can hence benefit from certain properties of AOP. Aspect-oriented monitor definitions are largely declarative, and hence tend to be simpler to reason about than instrumentation expressed through a program transformation written in an imperative programming language. Further, static analyses can reason about monitoring aspects more easily than about other forms of program instrumentation, as all instrumentation is contained within a single aspect. Many current runtime verification tools are hence built in the form of specification compilers, that take an expressive high-level specification as input and produce as output code written in some Aspect-oriented programming language (such as AspectJ).
Runtime verification, if used in combination with provably correct recovery code, can provide an invaluable infrastructure for program verification, which can significantly lower the latter's complexity. For example, formally verifying heap-sort algorithm is very challenging. One less challenging technique to verify it is to monitor its output to be sorted (a linear complexity monitor) and, if not sorted, then sort it using some easily verifiable procedure, say insertion sort. The resulting sorting program is now more easily verifiable, the only thing being required from heap-sort is that it does not destroy the original elements regarded as a multiset, which is much easier to prove. Looking at from the other direction, one can use formal verification to reduce the overhead of runtime verification, as already mentioned above for static analysis instead of formal verification. Indeed, one can start with a fully runtime verified, but probably slow program. Then one can use formal verification (or static analysis) to discharge monitors, same way a compiler uses static analysis to discharge runtime checks of type correctness or memory safety.
Compared to the more traditional verification approaches, an immediate disadvantage of runtime verification is its reduced coverage. This is not problematic when the runtime monitors are deployed with the system (together with appropriate recovery code to be executed when the property is violated), but it may limit the effectiveness of runtime verification when used to find errors in systems. Techniques to increase the coverage of runtime verification for error detection purposes include: