We provide a new technique for pattern matching that is based on components for each match. The set of match statements and their order is open for configuration at the right time and takes place in a feature-oriented fashion. This gives rise to a solution to the Expression Problem in presence of defaults. It takes a lightweight discipline to develop components for our technique. Their use for configuration of the pattern match, however, is virtually automatic.

Implementing Programming Languages (PLs) has always been a challenge for various reasons. One reason is the excess of routine tasks to be redone on every implementation cycle. This is despite the remarkable fraction of syntax and semantics usually shared between successive cycles. In this paper, we present a component-based approach to avoid reimplementation of shared PL fractions. We provide two sets of reusable components; one for syntax implementation and another for semantics. Our syntax and semantics components correspond to syntactic categories and semantics rules of a PL specification, respectively. We show how, in addition to their service to reusability in syntax and semantics, our components can cater reusable implementation of PL analyses. Our current level of experimentation suggests that this approach is applicable wherever the following two features are available or can be simulated: Type Constraints and Multiple Inheritance. Implementing a PL using our approach, however, requires some modest programming discipline that we will explain throughout the text.

We solve the Expression Compatibility Problem (ECP) -a variation of the famous Expression Problem (EP) which, in addition to the classical EP concerns, takes into consideration the replacement, refinement, and borrowing of algebraic datatype (ADT) cases. ECP describes ADT cases as components and promotes ideas from Lightweight Family Polymorphism, Class Sharing, and Expression Families Problem (EFP). Our solution is based on a formal model for Component-Based Software Engineering that pertains to EP. We provide the syntax, static semantics, and dynamic semantics of our model. We also show that our model solves EFP as well. Moreover, we show how to embed the model in Scala.

This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort, and money on detailed design and implementation. We describe ΔQSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ΔQSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ΔQSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way so that approximate results can be obtained quickly and subsequently refined. ΔQSD has been successfully used by a small team in Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ΔQSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ΔQSD has supported the development of an industry-leading proof-of-stake blockchain implementation that reliably and consistently delivers blocks of up to 80 kB every 20 s on average across a globally distributed network of collaborating block-producing nodes operating on the public internet.