This research-to-practice paper shows how to visualize the conceptual framework of object-oriented programming. 'Classes' and 'objects' (from the solution domain) are respectively visualized as ‘phenomena’ and 'concepts' (from the problem domain), thereby visualizing the connection between code and reality. In the spirit of bringing research-to-practice, we implemented a prototype programming environment for Java called ‘SHowMyCODE’ based on this conceptual visualization. We report on a controlled experiment involving N=138 introductory programming (CS1) students. The experiment involves three tasks with progression (USE, Modify, and Create) and compares a treatment group with the reality-code visualization vs a control group without the visualization. The results show that, for Use tasks, students are faster (but not more accurate) when using the reality-code visualisation. In contrast, for Create tasks, learners are more accurate (but not faster) when using the reality visualisation. Finally, we report that students appear to value a visualization that shows the reality-code connection above a generic visualization without this connection.

Exploring and comprehending a software system, e.g., as preparation for its re-engineering, is a relevant, yet challenging endeavour often conducted by teams of engineers. Collaborative exploration tools aim to ease the process, e.g., via interactive visualizations in virtual reality (VR). However, these neglect to provide engineers with capabilities for persisting their thoughts and findings.

Understanding software systems is a vital task, often undertaken by teams of engineers, for the development and maintenance of systems. Collaborative software visualization tools are essential in this context, yet they are limited. Existing tools, particularly in virtual reality, allow exploration but lack the crucial feature of note-taking, which is a significant limitation.

Comprehending large-scale software systems is a challenging and daunting task, particularly when only source code is available. While software visualization attempts to aid that process, existing tools primarily visualize a system's structure in terms of files, folders, packages, or namespaces, neglecting its logical decomposition into cohesive architectural components. We present the tool Immersive Software Archaeology (ISA) which (i) estimates a view of a system's architecture by utilizing concepts from software architecture recovery and (ii) visualizes the results in virtual reality (VR) so that users can explore a subject system interactively, making the process more engaging. In VR, a semantic zoom lets users gradually transition between architectural components of different granularity and class-level elements while relationship graphs let users navigate along connections across classes and architectural components. We present results from a controlled experiment with 54 partic-ipants to investigate the usefulness of ISA for assisting engineers with exploring an unfamiliar large-scale system compared to another state-of-the-art VR approach and an IDE. Video Demonstration-https://youtu.belFl_SsT1314k

Re-architecting a software system requires significant preparation, e.g., to scope and design new modules with their boundaries and constituent classes. When planning an intended future state of a system as a re-engineering goal, engineers often fall recur to mechanisms such as freehand sketching (using a whiteboard). While this ensures flexibility and expressiveness, the sketches remain disconnected from the source code. The alternative, tool-supported diagramming on the other hand considerably restricts flexibility and impedes free-form communication.We present a method for preparing the architectural software re-engineering via freehand sketches in virtual reality (VR) that can be seamlessly integrated with the model structure of a software visualization and, thus, also the code of a system, for productive use: Engineers explore a subject system in the immersive visualization, while freehand sketching their insights and plans. Our concept automatically interprets sketched shapes and connects them to the system’s source code, and superimposes code-level references into a sketch to support engineers in reflecting on their sketches.We evaluated our method in an iterative interview-based case study with software developers from four different companies, where they planned a hypothetical re-engineering of an open-source software system.

Domain-specific languages (DSLs) have demonstrated their usefulness within many domains such as finance, robotics, and telecommunication. This success has been exemplified by the publication of a wide range of articles regarding specific DSLs and their merits in terms of improved software quality, programmer efficiency, security, etc. However, there is little public information on what happens to these DSLs after they are developed and published. The lack of information makes it difficult for a DSL practitioner or tool creator to identify trends, current practices, and issues within the field. In this paper, we seek to establish the current state of a DSL's life cycle by analysing 30 questionnaire answers from DSL authors on the design and development, launch, evolution, and end of life of their DSL. On this empirical foundation, we make six recommendations to DSL practitioners, scholars, and tool creators on the subjects of user involvement in the design process, DSL evolution, and the end of life of DSLs.

Exploring an unfamiliar large-scale software system is challenging, especially when based solely on source code. While software visualizations help in gaining an overview of a system, they generally neglect architecture knowledge in their representations, e.g., by arranging elements along package structures rather than functional components or locking users in a specific abstraction only slightly above the source code. In this paper, we introduce an automated approach for software architecture recovery and use its results in an immersive 3D virtual reality software visualization to aid accessing and relating architecture knowledge. We further provide a semantic zoom that allows a user to access and relate information both horizontally on the same abstraction level, e.g., by following method calls, and vertically across different abstraction levels, e.g., from a class to its containing component. We evaluate our contribution in a controlled experiment contrasting the usefulness regarding software exploration and comprehension of our concepts with those of the established CityVR visualization and the Eclipse IDE.

Abstract In model-based systems engineering projects, engineers from multiple domains collaborate by establishing a common system model. Multi-level modeling is a technique that can be used to model the development from abstract ideas to concrete implementations. However, current multi-level modeling approaches are not adequate for processes with multiple modeling phases that might have to be rearranged later. In this paper, we introduce multi-phase modeling that utilizes concepts of multi-level modeling by considering a description of the expected phase ordering per domain. Constraints aware of this context can express that certain elements are only valid in specific phases without having to determine a concrete phase ordering for a particular model. This enables using multi-phase modeling in flexible workflows, adapting to changing requirements and the definition of access rules in domain notation. We show feasibility of this multi-phase modeling by applying it to multiple real-life systems engineering projects of the aerospace domain.

Abstract A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated for configurations consisting of selected features. Product-line evolution can lead to unintended changes to product behavior. We illustrate that updating configurations after product-line evolution requires decisions of both, domain engineers responsible for product-line evolution as well as application engineers responsible for configurations. The challenge is that domain and application engineers might not be able to interact with each other. We propose a formal foundation and a methodology that enables domain engineers to guide application engineers through configuration evolution by sharing knowledge on product-line evolution and by defining automatic update operations for configurations. As an effect, we enable knowledge transfer between those engineers without the need for interactions. We evaluate our methodology on four large-scale industrial product lines. The results of the qualitative evaluation indicate that our method is flexible enough for real-world product-line evolution. The quantitative evaluation indicates that we detect product behavior changes for up to $$55.3\%$$ 55.3 % of the configurations which would not have been detected using existing methods.

Domain-specific languages seek to provide domain guarantees that eliminate many errors allowed by general-purpose languages. Still, a domain-specific language requires additional quality assurance measures to ensure that specifications behave as intended by the users. However, some domains may have specific quality assurance measures (e.g., proofs, experiments, or case studies) with little tradition of using quality assurance measures customary to software engineering. We investigate the possibility of accommodating such domains by conducting a workshop with 11 prospective users of a domain-specific language named MAL for the pension industry. The workshop emphasised the need for supporting actuaries with new analytical tools for quality assurance and resulted in three designs: quantity monitors let users identify outlier behaviour, fragment debugging lets users debug with limited evaluative power, and debugging spreadsheets let users visualise, analyse, and remodel concrete calculations with an established domain tool. Based on our experiences, we hypothesise that co-design workshops are a viable approach for DSLs in a similar situation.

Many of today's software systems will become the legacy systems of tomorrow, comprised of outdated technology and inaccurate design documents. Preparing for their eventual re-engineering requires engineers to regain lost design knowledge and discover re-engineering opportunities. While tools and visualizations exist, comprehending an unfamiliar code base remains challenging. Hence, software archaeology suffers from a considerable entry barrier as it requires expert knowledge, significant diligence, tenacity, and stamina. In this paper, we propose a paradigm shift in how legacy systems' design knowledge can be regained by presenting our vision for an immersive explorable software visualization in virtual reality (VR). We propose innovative concepts leveraging benefits of VR for a) immersion in an exoteric visualization metaphor, b) effective navigation and orientation, c) guiding exploration, and d) maintaining a link to the implementation. By enabling immersive and playful legacy system exploration, we strive for lowering the entry barrier, fostering long-term engagement, strengthening mental-model building, and improving knowledge retention in an effort to ease coping with the increased number of tomorrow's legacy systems.

In immersive augmented reality (IAR), users can wear a head-mounted display to see computer-generated images superimposed to their view of the world. IAR was shown to be beneficial across several domains, e.g., automotive, medicine, gaming and engineering, with positive impacts on, e.g., collaboration and communication. We think that IAR bears a great potential for software engineering but, as of yet, this research area has been neglected. In this vision paper, we elicit potentials and obstacles for the use of IAR in software engineering. We identify possible areas that can be supported with IAR technology by relating commonly discussed IAR improvements to typical software engineering tasks. We further demonstrate how innovative use of IAR technology may fundamentally improve typical activities of a software engineer through a comprehensive series of usage scenarios outlining practical application. Finally, we reflect on current limitations of IAR technology based on our scenarios and sketch research activities necessary to make our vision a reality. We consider this paper to be relevant to academia and industry alike in guiding the steps to innovative research and applications for IAR in software engineering.

A feature model (FM) describes commonalities and variability within a software product line (SPL) and represents the configuration options at one point in time. A temporal feature model (TFM) additionally represents FM evolution, e.g., the change history or the planning of future releases. The increasing number of different TFM notations hampers research collaborations due to a lack of interoperability regarding notations, editors, and analyses. We present a common API for TFMs, which provides the core of a TFM ecosystem, to harmonize notations. We identified the requirements for the API based on systematically classifying and comparing the capabilities of existing TFM approaches. Our approach allows to work seamlessly with different TFM notations to perform, track, analyze and replay evolution. Our evaluation investigates two research questions on the expressiveness (RQ1) and utility (RQ2) of our approach by presenting implementations for several existing FM and TFM notations and replaying evolution histories from two case study systems.

In model-based software engineering, models are central artifacts used for management, design and implementation. To meet new requirements, engineers need to plan and perform model evolution. So far, model evolution histories are captured using Version Control Systems (VCSs), e.g., Git. However, these systems are unsuitable for planning model evolution as they do not have a notion of future changes. Furthermore, formally assigning responsibilities to engineers for performing evolution of model parts is achieved by using additional tools for access control. To remedy these shortcomings, we provide a method to generate evolution-aware modeling notations by augmenting existing metamodels with concepts for capturing past and planned evolution as first-class entity. Our method enables engineers to seamlessly plan future model evolution while actively developing the current model state, both using a centralized access point for evolution. In our evaluation, we provide an implementation of our method in the tool TemporalRegulator3000, show applicability for real-world metamodels, and capture the entire evolution time line of corresponding models.

Software Product Lines (SPLs) are a common technique to capture families of software products in terms of commonalities and variabilities. On a conceptual level, functionality of an SPL is modeled in terms of features in Feature Models (FMs). As other software systems, SPLs and their FMs are subject to evolution that may lead to the introduction of anomalies (e.g., non-selectable features). To fix such anomalies, developers need to understand the cause for them. However, for large evolution histories and large SPLs, explanations may become very long and, as a consequence, hard to understand. In this paper, we present a method for anomaly detection and explanation that, by encoding the entire evolution history, identifies the evolution step of anomaly introduction and explains which of the performed evolution operations lead to it. In our evaluation, we show that our method significantly reduces the complexity of generated explanations.

In Model-Driven Software Development, models are automatically processed to support the creation, build, and execution of systems. A large variety of dedicated model-transformation languages exists, promising to efficiently realize the automated processing of models. To investigate the actual benefit of using such specialized languages, we performed a large-scale controlled experiment in which over 78 subjects solve 231 individual tasks using three languages. The experiment sheds light on commonalities and differences between model transformation languages (ATL, QVT-O) and on benefits of using them in common development tasks (comprehension, change, and creation) against a modern general-purpose language (Xtend). Our results show no statistically significant benefit of using a dedicated transformation language over a modern general-purpose language. However, we were able to identify several aspects of transformation programming where domain-specific transformation languages do appear to help, including copying objects, context identification, and conditioning the computation on types.

A Software Product Line (SPL) captures families of software products and its functionality is captured as features in a feature model. Similar to other software systems, SPLs and their feature models are subject to evolution. Temporal Feature Models (TFMs) are an extension to feature models that allow for engineers to model past feature-model evolution and plan future evolution. When planning future evolution of feature models, multiple evolution steps may be planned upfront but changed requirements may lead to retroactively introducing evolution steps into the planned evolution or changing already planned steps. As a consequence, inconsistencies, which we denote as evolution paradoxes, may arise leading to invalidity of already modeled future evolution steps. In this paper, we present first steps towards allowing to introduce intermediate evolution steps into planned evolution while preserving consistency of all future evolution steps. To this end, we outline a method to define and check model evolution consistency rules. Using this method, engineers are allowed to introduce intermediate feature-model evolution steps whenever these changes preserve the evolution consistency rules.

A Software Product Line (SPL) is an approach to reuse in-the-large that models closely related software systems in terms of commonalities and variabilities. Design patterns are best practices for addressing recurring design problems. When implementing an SPL, instances of certain design patterns are employed to handle variability, which makes these variability-aware design patterns a best practice for SPL design. However, a proactive SPL development method with design patterns is lacking. In our paper [1], we present a method to perform generative SPL development with design patterns.

Testing is a fundamental task to ensure software quality. Regression testing aims to ensure that changes to software do not introduce new failures. As resources are often limited and testing comprises a vast amount of test cases, different regression strategies have been proposed to reduce testing effort by selecting or prioritizing important test cases, e.g., code coverage (to ensure a sufficient testing depth). However, in system testing, source code is often not available creating a black-box system. In this paper, we introduce an automated, multi-objective test case selection technique in black-box systems using genetic algorithms. We define seven different objectives, based on meta-data, allowing a flexible test case selection for a variety of systems. For evaluation, we apply our technique on two different subject systems assessing the feasibility and suitability of our test case selection approach. Results indicate that our approach is applicable based on different data available and is able to outperform random test case selection and retest-all.

Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.

Block-based modeling languages, such as MATLAB/Simulink or state charts, reduce the complexity inherent to developing large-scale software systems. When creating variants for largely similar yet different software systems, the common practice is to copy models and modify them to different requirements. While this allows companies to save costs in the short-term, these so-called clone-and-own approaches cause problems regarding long-term evolution and system quality as the relation between the variants of the resulting software family is lost so that the variants have to be maintained in isolation. To recreate information regarding the variants' relations, variability mining identifies common and varying parts of cloned variants but, currently, the respective algorithms have to be created for each target language individually. In this paper, we present a generalized method to instantiate variability mining for arbitrary block-based modeling languages. The identified variability information allows developers to understand the variability of their grown software family. This knowledge helps efficiently maintaining the variants and allows migrating from clone-and-own approaches to more elaborate reuse strategies, such as software product lines. We demonstrate the feasibility of our method by instantiating variability mining techniques for two block-based languages.

Software Product Lines (SPLs) are an approach to reuse in-the-large that models a set of closely related software systems in terms of commonalities and variabilities. Design patterns are best practices for addressing recurring design problems in object-oriented source code. In the practice of implementing an SPL, instances of certain design patterns are employed to handle variability, which makes these "variability-aware design patterns" a best practice for SPL design. However, there currently is no dedicated method for proactively developing SPL using design patterns suitable for realizing variable functionality. In this paper, we present a method to perform generative SPL development with design patterns. We use role models to capture design patterns and their relation to a variability model. We further allow mapping of individual design pattern roles to elements of realization artifacts to be generated (e.g., classes, methods) and check the conformance of the realization with the specification of the pattern. With this method, we support proactive development of SPL using design patterns to apply best practices for the realization of variability. We present an implementation of our approach within the Eclipse IDE and demonstrate it within a case study.

To support users in operating a computer system with a varying set of functions, it is fundamental to understand their intentions, e.g., within an in-car infotainment system. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling user intentions is currently not supported. However, manually creating software that predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user's intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible intentions of users according to the current situation. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user's behavior. We demonstrate feasibility of our approach using an industrial example of an intention-aware in-car infotainment system.

To cope with project constraints, copying and customizing existing software products is a typical practice to flexibly serve customer-specific needs. In the long term, this practice becomes a limitation for growth due to redundant maintenance efforts or wasted synergy and cross selling potentials. To mitigate this limitation, customized copies need to be consolidated into a single, variable code base of a software product line (SPL). However, consolidation is tedious as one must identify and correlate differences between the copies to design future variability. For one, existing consolidation approaches lack support of the implementation level. In addition, approaches in the fields of difference analysis and feature detection are not sufficiently integrated for finding relationships between code modifications. In this paper, we present remedy to this problem by integrating a difference analysis with a program dependency analysis based on Program Dependency Graphs (PDG) to reduce the effort of consolidating developers when identifying dependent differences and deriving clusters to consider in their variability design. We successfully evaluated our approach on variants of the open source ArgoUML modeling tool, reducing the manual review effort about 72% with a precision of 99% and a recall of 80%. We further proved its industrial applicability in a case study on a commercial relationship management application.

Software product lines (SPLs) and software ecosystems (SECOs) encompass a family of closely related software systems in terms of common and variable assets that are configured to concrete products (variability in space). Over the course of time, variable assets of SPLs and especially SECOs are subject to change in order to meet new requirements as part of software evolution (variability in time). Even though both dimensions of variability have to be handled simultaneously, e.g., as not all customers upgrade their respective products immediately or completely, there currently is no approach that can create variants with a selection of variable assets in various versions. In this paper, we introduce an integrated approach to manage variability in space and time in software families using Hyper Feature Models (HFMs) with feature versions and combine them with an extension of the transformational variability realization mechanism delta modeling. This allows derivation of concrete software systems from an SPL or SECO configuring both functionality (features) as well as versions.

Software Product Lines (SPLs) are a successful approach to software reuse in the large. Even though tools exist to create SPLs, their evolution is widely unexplored. Evolving an SPL manually is tedious and error-prone as it is hard to avoid unintended side-effects that may harm the consistency of the SPL. The main contribution of this paper is the conceptual basis of a system for the evolution of model-based SPLs, which maintains consistency of models and feature mapping. As further contribution, a novel classification is introduced that distinguishes evolutions by their potential to harm the mapping of an SPL. In addition, multiple remapping operators are presented that can remedy the negative side-effects of evolutions in order to co-evolve the feature mapping. Finally, an implementation of the evolution system in the SPL tool FeatureMapper is provided to demonstrate the capabilities of the presented approach when co-evolving models and feature mapping of an SPL.