Covariant Conversions (CoCo): A Design Pattern for Type-Safe Modular Software Evolution in Object-Oriented Systems

Software evolution is an essential challenge for all software engineers, typically addressed solely using code versioning systems and language-specific code analysis tools. Most versioning systems view the evolution of a system as a directed acyclic graph of steps, with independent branches that could be merged. What these systems fail to provide is the ability to ensure stable APIs or that each subsequent evolution represents a cohesive extension yielding a valid system. Modular software evolution ensures that APIs remain stable, which is achieved by ensuring that only additional methods, fields, and data types are added, while treating existing modules through blackbox interfaces. Even with these restrictions, it must be possible to add new variations, fields, and methods without extensive duplication of prior module code. In contrast to most literature, our focus is on ensuring modular software evolution using mainstream object-oriented programming languages, instead of resorting to novel language extensions. We present a novel CoCo design pattern that supports type-safe covariantly overridden convert methods to transform earlier data type instances into their newest evolutionary representation to access operations that had been added later. CoCo supports both binary methods and producer methods. We validate and contrast our approach using a well-known compiler construction case study that other researchers have also investigated for modular evolution. Our resulting implementation relies on less boilerplate code, is completely type-safe, and allows clients to use normal object-oriented calling conventions. We also compare CoCo with existing approaches to the Expression Problem. We conclude by discussing how CoCo could change the direction of currently proposed Java language extensions to support closed-world assumptions about data types, as borrowed from functional programming

Call Graphs for Languages with Parametric Polymorphism

The performance of contemporary object oriented languages depends on optimizations such as devirtualization, inlining, and specialization, and these in turn depend on precise call graph analysis. Existing call graph analyses do not take advantage of the information provided by the rich type systems of contemporary languages, in particular generic type arguments. Many existing approaches analyze Java bytecode, in which generic types have been erased. This paper shows that this discarded information is actually very useful as the context in a context-sensitive analysis, where it significantly improves precision and keeps the running time small. Specifically, we propose and evaluate call graph construction algorithms in which the contexts of a method are (i) the type arguments passed to its type parameters, and (ii) the static types of the arguments passed to its term parameters. The use of static types from the caller as context is effective because it allows more precise dispatch of call sites inside the callee. Our evaluation indicates that the average number of contexts required per method is small. We implement the analysis in the Dotty compiler for Scala, and evaluate it on programs that use the type-parametric Scala collections library and on the Dotty compiler itself. The context-sensitive analysis runs 1.4x faster than a context-insensitive one and discovers 20\% more monomorphic call sites at the same time. When applied to method specialization, the imprecision in a context-insensitive call graph would require the average method to be cloned 22 times, whereas the context-sensitive call graph indicates a much more practical 1.00 to 1.50 clones per method

Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.Comment: Accepted by ICPC-202

The Design and Implementation of Bloqqi - A Feature-Based Diagram Programming Language

This dissertation presents the design and implementation of a new block diagram programming language, Bloqqi, for building control systems with focus on variability. The language has been developed in collaboration with industry with the goal of reducing engineering time and improving reuse of functionality.When building a control system for a plant, there are typically different variants of the same base functionality. A plant may have several variants of a tank, for example, one variant with heating and another one without. This dissertation presents novel language mechanisms for describing this kind of variability, based on diagram inheritance. For instance, Bloqqi supports specifying what features, like heating, the base functionality can have. These specifications are then used to automatically derive smart-editing support in the form of a feature-based wizard. In this wizard, the user can select what features the base functionality should have, and code is generated corresponding to these features. The new language mechanisms allow feature-based libraries to be created and extended in a modular way.This dissertation presents techniques for implementing rich graphical editors with smart editing support based on semantic analysis. A prototype compiler and graphical editor have been implemented for the language, using the semantic formalism reference attribute grammars (RAGs). RAGs allow tools to share the semantic specifications, which makes it possible to modularly extend the compiler with support for advanced semantic feedback to the user of the graphical editor