Refactoring large legacy codebases, even with industrial-strength tools, often leads to trust concerns with code owners, in particular when the codebase underwent significant changes. To provide more assurance to code owners, we integrate visual analytics into the refactoring process. This method involves transforming code into control flow graphs before and after refactoring, followed by trace equivalence analysis on these graphs. An innovative visualisation tool provides not only a comprehensive overview of the refactorings' impact across all files, but also offers detailed insights into the trace equivalence at individual file level. By presenting clear visual evidence of code equivalence before and after refactoring, our visualisation narrows the trust gap, offering refactoring experts and code owners a transparent and understandable view of the changes. We apply this visualisation on an industrial use case and discuss its effectiveness with refactoring experts.

We propose a tool and underlying technique that uses semi-parsing to extract control flow graphs from legacy source code (i.e., COBOL). Obtaining such control flow graphs is relevant in the industrial setting of legacy modernisation, to quickly demonstrate to code owners that modernisation engineers did not break their business logic. They need to be convinced that a migration did not affect the flow around critical parts of their code such as database accesses. Focusing on the control flow around embedded SQL queries and confirming that the code logic has been preserved improves customers' trust and satisfaction in the modernisation. Our proposed algorithm and approach uses fuzzy parsing as opposed to full parsing to parse mainly the control flow constructs, while delegating the full parsing of embedded languages like SQL to an external parser, and produces a control flow graph directly while skipping over most of the input in linear time. Such a fuzzy parser is easier to construct and adapt to particular languages and needs than a full parser with a visitor to elicit control flow. Comparisons are made of the fuzzy parser to an industrial-strength full parser.

In programming education, explicit strategies are gaining traction. The reason for this study was to improve an introductory programming course based on a problem-based methodology, by using more explicit programming strategies. After analysing a previous run of this course for first year undergraduate students, we concluded that such strategies could improve learning transfer for students across the different weeks of the semester. We introduced four instructional strategies to tutors with close to no pedagogical background: explicit tracing, subgoal labeled worked examples, Parsons problems and explicit problem solving. These explicit programming strategies aim to decrease cognitive load. Tutors tested these four strategies in the course.

Large code refactoring projects can consist of hundreds of refactoring rules that are applied iteratively to make code easier to maintain. Visualising the refactoring process can help engineers and stakeholders understand how chains of refactorings were applied and to gain more confidence in the produced result. An apparently suitable existing visualisation using log-based behavioural differencing suffers from scalability issues when applied to industrial-size cases. We propose an adapted visualisation tool that highlights those parts that really changed in-between iterations of a large refactoring process and collapses those parts that remain stable. We show that our alternative visualisation scales well on large logs of a process with many possible refactoring chains, of which significant parts are shared. Consequently, it allows engineers and stakeholders to quickly answer relevant questions about what happened during the refactoring process.

In software engineering, a smell is a part of a software system's source code with a poor quality and that may indicate a deeper problem. Although many kinds of smells have been studied to analyze their causes, their behavior, and their impact on software quality, those smells typically are studied independently from each other. However, if two smells coincide inside a class, this could increases their negative effect (e.g., spaghetti code that is being cloned across the system). In this paper we report results from an empirical study conducted to examine the relationship between two specific kinds of smells: code clones and antipatterns. We conducted our study on three open-source software systems: Azureus, Eclipse, and JHotDraw. Results show that between 32% and 63% of classes in the analysed systems present co-occurrence of smells, and that such classes are more risky in term of fault-proneness.

Parsing Expression Grammars (PEGs) define languages by specifying a recursive-descent parser that recognises them. The PEG formalism exhibits desirable properties, such as closure under composition, built-in disambiguation, unification of syntactic and lexical concerns, and closely matching programmer intuition. Unfortunately, state of the art PEG parsers struggle with left-recursive grammar rules, which are not supported by the original definition of the formalism and can lead to infinite recursion under naive implementations. Likewise, support for associativity and explicit precedence is spotty. To remedy these issues, we introduce Autumn, a general purpose PEG library that supports left-recursion, left and right associativity and precedence rules, and does so efficiently. Furthermore, we identify infix and postfix operators as a major source of inefficiency in left-recursive PEG parsers and show how to tackle this problem. We also explore the extensibility of the PEG paradigm by showing how one can easily introduce new parsing operators and how our parser accommodates custom memoization and error handling strategies. We compare our parser to both state of the art and battle-tested PEG and CFG parsers, such as Rats!, Parboiled and ANTLR.

The development of self-adaptive systems requires the definition of the parts of the system that will be adapted, when such adaptations will take place, and how these adaptations will interact with each other. However, foreseeing all possible adaptations and their interactions is a difficult task, opening the possibility to inconsistencies or erroneous system behavior. To avoid inconsistencies, self adaptive systems require a sound programming model that allows to reason about the correctness of the system in spite of its dynamic reconfigurations. This paper presents context Petri nets, a Petri net-based programming model for selfadaptive systems. This model provides a formal definition of adaptations and their interaction, as well as a consistent process for their inclusion in the system. Besides serving as an underlying run-time model to ensure that adaptations and their constraints remain consistent, context Petri nets allow to analyze properties such as reachability, and liveness in the configuration of self-adaptive systems. Context Petri nets thus are a convenient tool to model and analyze the dynamics of self-adaptive systems, both formally and computationally.

When evolving software systems, developers spend a considerable amount of time understanding existing source code. To successfully implement new or alter existing behavior, developers need to answer questions such as: “Which types and methods can I use to solve this task?” or “Should my implementation follow particular naming or structural conventions?”. In this paper we present Mendel, a source code recommendation tool that aids developers in answering such questions. Based on the entity the developer currently browses, the tool employs a genetics-inspired metaphor to analyze source-code entities related to the current working context and provides its user with a number of recommended properties (naming conventions, used types, invoked messages, etc.) that the source code entity currently being worked on should exhibit. An initial validation of Mendel seems to confirm the potential of our approach.

This workshop will explore the contributions that software research can make to the challenge of tackling climate change. Software is a critical enabling technology in nearly all aspects of climate change, from the computational models used by climate scientists to improve our understanding of the impact of human activities on earth systems, through to the information and control systems needed to build an effective carbon-neutral society. The intent of the workshop is to explore how software research can contribute to this challenge, to build a community of researchers interested in responding to the challenge, and to map out a research agenda.

The design and implementation of a software system is often governed by many different coding conventions, design patterns, architectural design rules, and other so-called structural regularities. To prevent a deterioration of the system's source code, it is important that these regularities are verified and enforced in subsequent evolutions of the system. The Intensional Views Environment (IntensiVE), which is the subject of this demonstration, is a tool suite for documenting such structural regularities in (object-oriented) software systems and verifying their consistency in later versions of those systems.

Operations languages are used to write spacecraft operations procedures. The APPAREIL tool automates the process of generating program translators between operations languages, from a specification of their language grammar annotated with extra information. From these annotated grammars the tool automatically produces a partial translator that covers most of the translation. This translator needs to be augmented manually with specific transformations, to deal with the more complicated cases. To get more confidence on the correctness of the translation, the tool offers a control-flow equivalence verification module.

With the advent of ambient intelligence and advances in mobile hardware technology, the next generation of software systems will require the ability to gracefully and dynamically adapt to changes in their surrounding environment. Contemporary languages provide no dedicated support to this end, thus requiring software developers to achieve this run-time adaptability through the use of specific design patterns and architectural solutions. As a consequence, all possible variability points of mobile systems need to be anticipated up front. Instead, we aim at solving the problem at the language level. We propose a new programming language called Ambience that provides dedicated language mechanisms to manage changing contexts and deal with run-time adaptation of mobile applications to those contexts. The language abstractions we propose are based on a prototype-based programming model that features multimethods and subjective object behaviour. We illustrate and motivate our approach by means of two running examples that were implemented in our language.

As size and complexity of software systems increase, preserving the design and specification of their implementation structure gains importance in order to maintain the evolvability of the system. However, due to constant changes, the implementation structure and its documentation tend to dilute over time. Building on the underlying models of intensional views and intensional relations, our IntensiVE toolsuite helps a developer in documenting structural source-code regularities, verifying them and offering fine-grained feedback when the code does not satisfy those regularities.

The conceptual structure of existing software systems is often implicit or non-existing in the source code. We propose the lightweight abstraction of intentional source-code views as a means of making these conceptual structures more explicit. Based on the experience gained with two case studies, we illustrate how intentional source-code views can simplify and improve software understanding, maintenance and evolution in various ways. We present the results as a catalog of usage scenarios in a pattern-like format.

Maintaining the source code of large software systems is hard. One underlying cause is that existing modularisation mechanisms are inadequate to handle crosscutting concerns. We propose intentional source-code views as an intuitive and lightweight means of modelling such concerns. They increase our ability to understand, modularise and browse the source code by grouping together source-code entities that address the same concern. They facilitate software development and evolution, because alternative descriptions of the same intentional view can be checked for consistency and relations among intentional views can be defined and verified. Finally, they enable us to specify knowledge developers have about source code that is not captured by traditional program documentation mechanisms.Our intentional view model is implemented in a logic metaprogramming language that can reason about and manipulate object-oriented source code directly. The proposed model has been validated on the evolution of a medium-sized object-oriented application in Smalltalk, and a prototype tool has been implemented.

A critical concern in the reuse of software is the propagation of changes made to reusable artifacts. Without techniques to manage these changes, multiple versions of these artifacts will propagate through different systems and reusers will not be able to benefit from improvements to the original artifact. We propose to codify the management of change in a software system by means of reuse contracts that record the protocol between managers and users of a reusable asset. Just as real world contracts can be extended, amended and customised, reuse contracts are subject to parallel changes encoded by formal reuse operators: extension, refinement and concretisation. Reuse contracts and their operators serve as structured documentation and facilitate the propagation of changes to reusable assets by indicating how much work is needed to update previously built applications, where and how to test and how to adjust these applications.