Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Improving Software Project Health Using Machine Learning

In recent years, systems that would previously live on different platforms have been integrated under a single umbrella. The increased use of GitHub, which offers pull-requests, issue trackingand version history, and its integration with other solutions such as Gerrit, or Travis, as well as theresponse from competitors, created development environments that favour agile methodologiesby increasingly automating non-coding tasks: automated build systems, automated issue triagingetc. In essence, source-code hosting platforms shifted to continuous integration/continuousdelivery (CI/CD) as a service. This facilitated a shift in development paradigms, adherents ofagile methodology can now adopt a CI/CD infrastructure more easily. This has also created large,publicly accessible sources of source-code together with related project artefacts: GHTorrent andsimilar datasets now offer programmatic access to the whole of GitHub. Project health encompasses traceability, documentation, adherence to coding conventions,tasks that reduce maintenance costs and increase accountability, but may not directly impactfeatures. Overfocus on health can slow velocity (new feature delivery) so the Agile Manifestosuggests developers should travel light — forgo tasks focused on a project health in favourof higher feature velocity. Obviously, injudiciously following this suggestion can undermine aproject’s chances for success. Simultaneously, this shift to CI/CD has allowed the proliferation of Natural Language orNatural Language and Formal Language textual artefacts that are programmatically accessible:GitHub and their competitors allow API access to their infrastructure to enable the creation ofCI/CD bots. This suggests that approaches from Natural Language Processing and MachineLearning are now feasible and indeed desirable. This thesis aims to (semi-)automate tasks forthis new paradigm and its attendant infrastructure by bringing to the foreground the relevant NLPand ML techniques. Under this umbrella, I focus on three synergistic tasks from this domain: (1) improving theissue-pull-request traceability, which can aid existing systems to automatically curate the issuebacklog as pull-requests are merged; (2) untangling commits in a version history, which canaid the beforementioned traceability task as well as improve the usability of determining a faultintroducing commit, or cherry-picking via tools such as git bisect; (3) mixed-text parsing, to allowbetter API mining and open new avenues for project-specific code-recommendation tools

A network transparent, retained mode multimedia processing framework for the Linux operating system environment

Die Arbeit präsentiert ein Multimedia-Framework für Linux, das im Unterschied zu früheren Arbeiten auf den Ideen "retained-mode processing" und "lazy evaluation" basiert: Statt Transformationen unmittelbar auszuführen, wird eine abstrakte Repräsentation aller Medienelemente aufgebaut. "renderer"-Treiber fungieren als Übersetzer, die diese Darstellung zur Laufzeit in konkrete Operationen umsetzen, wobei das Datenmodell zahlreiche Optimierungen zur Reduktion der Anzahl der Schritte oder der Minimierung von Kommunikation erlaubt. Dies erlaubt ein stark vereinfachtes Programmiermodell bei gleichzeitiger Effizienzsteigerung. "renderer"-Treiber können zur Ausführung von Transformationen den lokalen Prozessor verwenden, oder können die Operationen delegieren. In der Arbeit wird eine Erweiterung des X Window Systems um Mechanismen zur Medienverarbeitung vorgestellt, sowie ein "renderer"-Treiber, der diese zur Delegation der Verarbeitung nutzt

Search-Based Software Maintenance and Testing

2012 - 2013In software engineering there are many expensive tasks that are performed during development and maintenance activities. Therefore, there has been a lot of e ort to try to automate these tasks in order to signi cantly reduce the development and maintenance cost of software, since the automation would require less human resources. One of the most used way to make such an automation is the Search-Based Software Engineering (SBSE), which reformulates traditional software engineering tasks as search problems. In SBSE the set of all candidate solutions to the problem de nes the search space while a tness function di erentiates between candidate solutions providing a guidance to the optimization process. After the reformulation of software engineering tasks as optimization problems, search algorithms are used to solve them. Several search algorithms have been used in literature, such as genetic algorithms, genetic programming, simulated annealing, hill climbing (gradient descent), greedy algorithms, particle swarm and ant colony. This thesis investigates and proposes the usage of search based approaches to reduce the e ort of software maintenance and software testing with particular attention to four main activities: (i) program comprehension; (ii) defect prediction; (iii) test data generation and (iv) test suite optimiza- tion for regression testing. For program comprehension and defect prediction, this thesis provided their rst formulations as optimization problems and then proposed the usage of genetic algorithms to solve them. More precisely, this thesis investigates the peculiarity of source code against textual documents written in natural language and proposes the usage of Genetic Algorithms (GAs) in order to calibrate and assemble IR-techniques for di erent software engineering tasks. This thesis also investigates and proposes the usage of Multi-Objective Genetic Algorithms (MOGAs) in or- der to build multi-objective defect prediction models that allows to identify defect-prone software components by taking into account multiple and practical software engineering criteria. Test data generation and test suite optimization have been extensively investigated as search- based problems in literature . However, despite the huge body of works on search algorithms applied to software testing, both (i) automatic test data generation and (ii) test suite optimization present several limitations and not always produce satisfying results. The success of evolutionary software testing techniques in general, and GAs in particular, depends on several factors. One of these factors is the level of diversity among the individuals in the population, which directly a ects the exploration ability of the search. For example, evolutionary test case generation techniques that employ GAs could be severely a ected by genetic drift, i.e., a loss of diversity between solutions, which lead to a premature convergence of GAs towards some local optima. For these reasons, this thesis investigate the role played by diversity preserving mechanisms on the performance of GAs and proposed a novel diversity mechanism based on Singular Value Decomposition and linear algebra. Then, this mechanism has been integrated within the standard GAs and evaluated for evolutionary test data generation. It has been also integrated within MOGAs and empirically evaluated for regression testing. [edited by author]XII n.s

Conservative and traceable executions of heterogeneous model management workflows

One challenge of developing large scale systems is knowing how artefacts are interrelated across tools and languages, especially when traceability is mandated e.g., by certifying authorities. Another challenge is the interoperability of all required tools to allow the software to be built, tested, and deployed efficiently as it evolves. Build systems have grown in popularity as they facilitate these activities. To cope with the complexities of the development process, engineers can adopt model-driven practices that allow them to raise the system abstraction level by modelling its domain, therefore, reducing the accidental complexity that comes from e.g., writing boilerplate code. However, model-driven practices come with challenges such as integrating heterogeneous model management tasks e.g., validation, and modelling technologies e.g., Simulink (a proprietary modelling environment for dynamic systems). While there are tools that support the execution of model-driven workflows, some support only specific modelling technologies, lack the generation of traceability information, or do not offer the cutting-edge features of build systems like conservative executions i.e., where only tasks affected by changes to resources are executed. In this work we propose ModelFlow, a workflow language and interpreter able to specify and execute model management workflows conservatively and produce traceability information as a side product. In addition, ModelFlow reduces the overhead of model loading and disposal operations by allowing model management tasks to share already loaded models during the workflow execution. Our evaluation shows that ModelFlow can perform conservative executions which can improve the performance times in some scenarios. ModelFlow is designed to support the execution of model management tasks targeting various modelling frameworks and can be used in conjunction with models from heterogeneous technologies. In addition to EMF models, ModelFlow can also handle Simulink models through a driver developed in the context of this thesis which was used to support one case study

Optimizing whole programs for code size

Reducing code size has benefits at every scale. It can help fit embedded software into strictly limited storage space, reduce mobile app download time, and improve the cache usage of supercomputer software. There are many optimizations available that reduce code size, but research has often neglected this goal in favor of speed, and some recently developed compiler techniques have not yet been applied for size reduction. My work shows that newly practical compiler techniques can be used to develop novel code size optimizations. These optimizations complement each other, and other existing methods, in minimizing code size. I introduce two new optimizations, Guided Linking and Semantic Outlining, and also present a comparison framework for code size reduction methods that explains how and when my new optimizations work well with other, existing optimizations. Guided Linking builds on recent work that optimizes multiple programs and shared libraries together. It links an arbitrary set of programs and libraries into a single module. The module can then be optimized with arbitrary existing link-time optimizations, without changes to the optimization code, allowing them to work across program and library boundaries; for example, a library function can be inlined into a plugin module. I also demonstrate that deduplicating functions in the merged module can significantly reduce code size in some cases. Guided Linking ensures that all necessary dynamic linker behavior, such as plugin loading, still works correctly; it relies on developer-provided constraints to indicate which behavior must be preserved. Guided Linking can achieve a 13% to 57% size reduction in some scenarios, and can speed up the Python interpreter by 9%. Semantic Outlining relies on the use of automated theorem provers to check semantic equivalence of pieces of code, which has only recently become feasible to perform at scale. It extends outlining, an established technique for deduplicating structurally equivalent pieces of code, to work on code pieces that are semantically equivalent even if their structure is completely different. My comparison framework covers a large number of different code size reduction methods from the literature, in addition to my new methods. It describes several different aspects by which each method can be compared; in particular, there are multiple types of redundancy in program code that can be exploited to reduce code size, and methods that exploit different types of redundancy are likely to work well in combination with each other. This explains why Guided Linking and Semantic Outlining can be effective when used together, along with some kinds of existing optimizations

Similarity and Diversity in Information Retrieval

Inter-document similarity is used for clustering, classification, and other purposes within information retrieval. In this thesis, we investigate several aspects of document similarity. In particular, we investigate the quality of several measures of inter-document similarity, providing a framework suitable for measuring and comparing the effectiveness of inter-document similarity measures. We also explore areas of research related to novelty and diversity in information retrieval. The goal of diversity and novelty is to be able to satisfy as many users as possible while simultaneously minimizing or eliminating duplicate and redundant information from search results. In order to evaluate the effectiveness of diversity-aware retrieval functions, user query logs and other information captured from user interactions with commercial search engines are mined and analyzed in order to uncover various informational aspects underlying queries, which are known as subtopics. We investigate the suitability of implicit associations between document content as an alternative to subtopic mining. We also explore subtopic mining from document anchor text and anchor links. In addition, we investigate the suitability of inter-document similarity as a measure for diversity-aware retrieval models, with the aim of using measured inter-document similarity as a replacement for diversity-aware evaluation models that rely on subtopic mining. Finally, we investigate the suitability and application of document similarity for requirements traceability. We present a fast algorithm that uncovers associations between various versions of frequently edited documents, even in the face of substantial changes