Modular Materialisation of Datalog Programs

The semina\"ive algorithm can materialise all consequences of arbitrary datalog rules, and it also forms the basis for incremental algorithms that update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for materialisation computation and its maintenance. We split a datalog program into modules that can be handled using specialised algorithms, and handle the remaining rules using the semina\"ive algorithm. We also present two algorithms for computing the transitive and the symmetric-transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.Comment: Accepted at AAAI 201

Run-time Variability with First-class Contexts

Software must be regularly updated to keep up with changing requirements. Unfortunately, to install an update, the system must usually be restarted, which is inconvenient and costly. In this dissertation, we aim at overcoming the need for restart by enabling run-time changes at the programming language level. We argue that the best way to achieve this goal is to improve the support for encapsulation, information hiding and late binding by contextualizing behavior. In our approach, behavioral variations are encapsulated into context objects that alter the behavior of other objects locally. We present three contextual language features that demonstrate our approach. First, we present a feature to evolve software by scoping variations to threads. This way, arbitrary objects can be substituted over time without compromising safety. Second, we present a variant of dynamic proxies that operate by delegation instead of forwarding. The proxies can be used as building blocks to implement contextualization mechanisms from within the language. Third, we contextualize the behavior of objects to intercept exchanges of references between objects. This approach scales information hiding from objects to aggregates. The three language features are supported by formalizations and case studies, showing their soundness and practicality. With these three complementary language features, developers can easily design applications that can accommodate run-time changes

Models of higher-order, type-safe, distributed computation over autonomous persistent object stores

A remote procedure call (RPC) mechanism permits the calling of procedures in another address space. RPC is a simple but highly effective mechanism for interprocess communication and enjoys nowadays a great popularity as a tool for building distributed applications. This popularity is partly a result of their overall simplicity but also partly a consequence of more than 20 years of research in transpaxent distribution that have failed to deliver systems that meet the expectations of real-world application programmers. During the same 20 years, persistent systems have proved their suitability for building complex database applications by seamlessly integrating features traditionally found in database management systems into the programming language itself. Some research. effort has been invested on distributed persistent systems, but the outcomes commonly suffer from the same problems found with transparent distribution. In this thesis I claim that a higher-order persistent RPC is useful for building distributed persistent applications. The proposed mechanism is: realistic in the sense that it uses current technology and tolerates partial failures; understandable by application programmers; and general to support the development of many classes of distributed persistent applications. In order to demonstrate the validity of these claims, I propose and have implemented three models for distributed higher-order computation over autonomous persistent stores. Each model has successively exposed new problems which have then been overcome by the next model. Together, the three models provide a general yet simple higher-order persistent RPC that is able to operate in realistic environments with partial failures. The real strength of this thesis is the demonstration of realism and simplicity. A higherorder persistent RPC was not only implemented but also used by programmers without experience of programming distributed applications. Furthermore, a distributed persistent application has been built using these models which would not have been feasible with a traditional (non-persistent) programming language

Scalable Automated Incrementalization for Real-Time Static Analyses

This thesis proposes a framework for easy development of static analyses, whose results are incrementalized to provide instantaneous feedback in an integrated development environment (IDE). Today, IDEs feature many tools that have static analyses as their foundation to assess software quality and catch correctness problems. Yet, these tools often fail to provide instantaneous feedback and are thus restricted to nightly build processes. This precludes developers from fixing issues at their inception time, i.e., when the problem and the developed solution are both still fresh in mind. In order to provide instantaneous feedback, incrementalization is a well-known technique that utilizes the fact that developers make only small changes to the code and, hence, analysis results can be re-computed fast based on these changes. Yet, incrementalization requires carefully crafted static analyses. Thus, a manual approach to incrementalization is unattractive. Automated incrementalization can alleviate these problems and allows analyses writers to formulate their analyses as queries with the full data set in mind, without worrying over the semantics of incremental changes. Existing approaches to automated incrementalization utilize standard technologies, such as deductive databases, that provide declarative query languages, yet also require to materialize the full dataset in main-memory, i.e., the memory is permanently blocked by the data required for the analyses. Other standard technologies such as relational databases offer better scalability due to persistence, yet require large transaction times for data. Both technologies are not a perfect match for integrating static analyses into an IDE, since the underlying data, i.e., the code base, is already persisted and managed by the IDE. Hence, transitioning the data into a database is redundant work. In this thesis a novel approach is proposed that provides a declarative query language and automated incrementalization, yet retains in memory only a necessary minimum of data, i.e., only the data that is required for the incrementalization. The approach allows to declare static analyses as incrementally maintained views, where the underlying formalism for incrementalization is the relational algebra with extensions for object-orientation and recursion. The algebra allows to deduce which data is the necessary minimum for incremental maintenance and indeed shows that many views are self-maintainable, i.e., do not require to materialize memory at all. In addition an optimization for the algebra is proposed that allows to widen the range of self-maintainable views, based on domain knowledge of the underlying data. The optimization works similar to declaring primary keys for databases, i.e., the optimization is declared on the schema of the data, and defines which data is incrementally maintained in the same scope. The scope makes all analyses (views) that correlate only data within the boundaries of the scope self-maintainable. The approach is implemented as an embedded domain specific language in a general-purpose programming language. The implementation can be understood as a database-like engine with an SQL-style query language and the execution semantics of the relational algebra. As such the system is a general purpose database-like query engine and can be used to incrementalize other domains than static analyses. To evaluate the approach a large variety of static analyses were sampled from real-world tools and formulated as incrementally maintained views in the implemented engine

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

Assessing architectural evolution: A case study

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach