Modern development environments promote live programming (LP) mechanisms because it enhances the development experience by providing instantaneous feedback and interaction with live objects. LP is typically supported with advanced reflective techniques within dynamic languages. These languages run on top of Virtual Machines (VMs) that are built in a static manner so that most of their components are bound at compile time. As a consequence, VM developers are forced to work using the traditional edit-compile-run cycle, even when they are designing LP-supporting environments. In this paper we explore the idea of bringing LP techniques to the VM domain for improving their observability, evolution and adaptability at run-time. We define the notion of fully reflective execution environments (EEs), systems that provide reflection not only at the application level but also at the level of the VM. We characterize such systems, propose a design, and present Mate v1, a prototypical implementation. Based on our prototype, we analyze the feasibility and applicability of incorporating reflective capabilities into different parts of EEs. Furthermore, the evaluation demonstrates the opportunities such reflective capabilities provide for unanticipated dynamic adaptation scenarios, benefiting thus, a wider range of users.

The R programming language combines a number of features considered hard to analyze and implement efficiently: dynamic typing, reflection, lazy evaluation, vectorized primitive types, first-class closures, and extensive use of native code. Additionally, variable scopes are reified at runtime as first-class environments. The combination of these features renders most static program analysis techniques impractical, and thus, compiler optimizations based on them ineffective. We present our work on PIR, an intermediate representation with explicit support for first-class environments and effectful lazy evaluation. We describe two dataflow analyses on PIR: the first enables reasoning about variables and their environments, and the second infers where arguments are evaluated. Leveraging their results, we show how to elide environment creation and inline functions.CCS Concepts • Software and its engineering → Compilers.

Programming language virtual machines (VMs) realize language semantics, enforce security properties, and execute applications efficiently. Fully Reflective Execution Environments (EEs) are VMs that additionally expose their whole structure and behavior to applications. This enables developers to observe and adapt VMs at run time. However, there is a belief that reflective EEs are not viable for practical usages because such flexibility would incur a high performance overhead.To refute this belief, we built a reflective EE on top of a highly optimizing dynamic compiler. We introduced a new optimization model that, based on the conjecture that variability of low-level (EE-level) reflective behavior is low in many scenarios, mitigates the most significant sources of the performance overheads related to the reflective capabilities in the EE. Our experiments indicate that reflective EEs can reach peak performance in the order of standard VMs. Concretely, that a) if reflective mechanisms are not used the execution overhead is negligible compared to standard VMs, b) VM operations can be redefined at language-level without incurring in significant overheads, c) for several software adaptation tasks, applying the reflection at the VM level is not only lightweight in terms of engineering effort, but also competitive in terms of performance in comparison to other ad-hoc solutions.

In order to generate efficient code, dynamic language compilers often need information, such as dynamic types, not readily available in the program source. Leveraging a mixture of static and dynamic information, these compilers speculate on the missing information. Within one compilation unit, they specialize the generated code to the previously observed behaviors, betting that past is prologue. When speculation fails, the execution must jump back to unoptimized code. In this paper, we propose an approach to further the specialization, by disentangling classes of behaviors into separate optimization units. With contextual dispatch, functions are versioned and each version is compiled under different assumptions. When a function is invoked, the implementation dispatches to a version optimized under assumptions matching the dynamic context of the call. As a proof-of-concept, we describe a compiler for the R language which uses this approach. Our implementation is, on average, 1.7× faster than the GNU R reference implementation. We evaluate contextual dispatch on a set of benchmarks and measure additional speedup, on top of traditional speculation with deoptimization techniques. In this setting contextual dispatch improves the performance of 18 out of 46 programs in our benchmark suite.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

VMs are complex pieces of software that implement programming language semantics in an efficient, portable, and secure way. Unfortunately, mainstream VMs provide applications with few mechanisms to alter execution semantics or memory management at run time. We argue that this limits the evolvability and maintainability of running systems for both, the application domain, e.g., to support unforeseen requirements, and the VM domain, e.g., to modify the organization of objects in memory. This work explores the idea of incorporating reflective capabilities into the VM domain and analyzes its impact in the context of software adaptation tasks. We characterize the notion of a fully reflective VM, a kind of VM that provides means for its own observability and modifiability at run time. This enables programming languages to adapt the underlying VM to changing requirements. We propose a reference architecture for such VMs and present TruffleMATE as a prototype for this architecture. We evaluate the mechanisms TruffleMATE provides to deal with unanticipated dynamic adaptation scenarios for security, optimization, and profiling aspects. In contrast to existing alternatives, we observe that TruffleMATE is able to handle all scenarios, using less than 50 lines of code for each, and without interfering with the application's logic.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

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