Electroencephalography (EEG)-based speech brain–computer interfaces (BCIs) have gained increasing research interest as a potential means to restore or decode speech for individuals with severe communication impairments, particularly with the recent integration of deep learning techniques. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, provides a comprehensive and quantitative overview of deep learning methods applied to EEG and intracranial EEG (iEEG) speech processing tasks published between 2018 and 2025. A systematic search was conducted across Scopus, IEEE Xplore, ScienceDirect, Web of Science, and PubMed, yielding 1,148 records. Following duplicate removal, screening, and eligibility assessment, 80 peer-reviewed original research articles were included. Studies were organized by task type (speech classification, spectrogram reconstruction, and speech synthesis), neural signal type (non-invasive EEG versus invasive electrocorticography (ECoG) and stereo-electroencephalography (sEEG)), and model architecture to enable structured comparison. Results demonstrate that deep learning models significantly outperform traditional methods in speech classification tasks, while spectrogram reconstruction and speech synthesis remain challenging, particularly for non-invasive EEG. Invasive recordings consistently yield superior performance for reconstruction and synthesis tasks. Despite methodological advances, only a limited number of studies address real-time feasibility or cross-subject generalization, highlighting persistent barriers to clinical translation. Overall, this review provides an integrated, task-oriented synthesis of deep learning–based neural speech decoding and highlights the key methodological and translational barriers that must be addressed to enable robust, real-time, and clinically viable speech neuroprosthetic systems.

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language ( UVL ), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels – Boolean, Arithmetic, and Type – and allows for language extensions to introduce additional constructs enhancing its expressiveness. UVL is integrated into various existing software tools, such as FeatureIDE and flamapy, and is maintained by a consortium of institutions. All tools that support the language are released in an open-source format, complemented by dedicated parser implementations for Python and Java. Beyond academia, UVL has found adoption within a range of institutions and companies. It is envisaged that UVL will become the language of choice in the future for a multitude of purposes, including knowledge sharing, educational instruction, and tool integration and interoperability. We envision UVL as a pivotal solution, addressing the limitations of prior attempts and fostering collaboration and innovation in the domain of software product line engineering.

The configuration space of some systems is so large that it cannot be computed. This is the case with the Linux Kernel , which provides more than 18,000 configurable options described across almost 1,700 files in the Kconfig language. As a result, many analyses of these systems rely on sampling their configuration space (e.g., debugging compilation errors, predicting configuration performance, finding the configuration that optimizes specific performance metrics, among others.). The Kernel and other Kconfig -based systems can be sampled pragmatically , using their built-in tool conf to get a sample directly from the Kconfig specification that is approximately random, or idealistically , generating a genuine random sample by first translating the Kconfig files into logic formulas, then using a logic engine to compute the probability that each option value has to appear in a configuration, and finally utilizing these probabilities to generate an authentically random sample. The pros of the idealistic approach are that it ensures the sample is representative of the population, but the cons are that it sets out many challenging problems that have not been solved yet (fundamentally, how to obtain a valid translation into Boolean that covers all the Kconfig language, and how to compute the option value probabilities for very large formulas). This paper introduces a new version of conf called randconfig + , which incorporates a series of improvements that increase the randomness and correctness of pragmatic sampling and also help validate the Boolean translation required for the idealistic approach. randconfig + has been tested on ten versions of the Linux Kernel and twenty additional Kconfig systems. Its compatibility significantly enhances the current landscape, where some systems use a customized conf variant that is maintained independently, while others do not support sampling at all. randconfig + not only offers universal sampling for all Kconfig systems but also simplifies its evolutive maintenance as a single tool rather than an unorganized collection of conf variants.

FM Fact Label is a tool for visualizing the characterizations of feature models based on their metadata, structural measures, and analytical metrics. Although there are various metrics available to characterize feature models, there is no standard method to visualize and identify unique properties of feature models. Unlike existing tools, FM Fact Label provides a standalone web-based platform for configurable and interactive visualization, enabling export to various formats. This contribution is significant because it supports the Universal Variability Language (UVL) and enhances the UVL ecosystem by offering a common representation of the results of existing analysis tools.

Software development projects are built on top of external libraries and tools that help manage code and databases and/or facilitate deployment. The external libraries that assist in these tasks create dependent relations with the developed software, thereby increasing the use of dependencies as a common practice. There exist mechanisms in the projects to set up software dependencies in terms of versions and restrictions between said projects. However, any problem, error, or vulnerability affecting a software's configuration dependencies can render the whole project vulnerable. This turns a secure dependency into an insecure dependency, and hinders the maintenance of security in software development projects, since current tools do not cover all possible configurations of dependencies. In this paper, our approach that enables the analysis and inference of the configuration of dependencies of projects in terms of potentially vulnerable configurations. The proposal is developed by constructing a dependency graph network attributed to vulnerabilities. Formal models are integrated based on Satisfiability Modulo Theories (SMT) to enable automatic analysis, such as the identification of the most secure configuration of dependencies. The automatic analysis facilitates ascertaining the vulnerability-free configurations of dependencies with maximum and minimum vulnerability impacts. This proposal has been evaluated by analysing more than 140 Python open-source code repositories and better results than other proposals have been achieved.

Software product line engineering aims to systematically generate similar products or services within a given domain to reduce cost and time to market while increasing reuse. Various studies recognize the success of product line engineering in different domains. Software variability have increased over the years in many different domains such as mobile applications, cyber–physical systems or car control systems to just mention a few. However, software product line engineering is not as widely adopted as other software development technologies. In this paper, we present an empirical study conducted through a survey distributed to many software development companies. Our goal is to understand their need of software variability management and the level of knowledge the companies have regarding software product line engineering. The survey was answered by 127 participants from more than a hundred of different software development companies. Our study reveals that most of companies manage a catalog of similar products in a way or another, they mostly document the features of products using text or spreed sheet based documents and more than 66% of companies identify a base product from which they derive other similar products. We also found a correlation between the lack of Software Product Line (SPL) knowledge and the absence of reuse practices. Notably, this is the first study that explore software variability needs regardless of a company’s prior knowledge of SPL. The results encourages further research to understand the reason for the limited knowledge and application of software product line engineering practices, despite the growing demand of variability management.

Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents , a repository of feature models in UVL format. provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.

Data visualization aims to convey quantitative and qualitative information effectively by determining which techniques and visualizations are most appropriate for different situations and why. Various software solutions can produce numerous visualizations of the same data set. However, data visualization encompasses a wide range of visual configurations that depend on factors such as the type of data being displayed, the different displays (e.g., scatter plots, line graphs, and pie charts), the visual components used to represent the data (e.g., lines, dots, and bars), and the specific visual attributes of those components (e.g., color, shape, size, and length). A similar problem arises when designing data tables, where the dimensionality of the data and its complexity influence the choice of the most appropriate structure (e.g., unidirectional, bidirectional). Often, this broad spectrum of configurations requires a visualization expert who knows which techniques are best for which type of data source and what is to be conveyed. Typically, researchers and developers lack knowledge of data visualization best practices and must learn the design principles that enable effective communication and the technical details of the specific software tool they use to generate visualizations. This paper proposes a software product line approach to model and realize the variability of the visualization design process, using feature models to encode knowledge about design best practices in graphs and charts. Our approach involves solving visualization design variability through a stepwise configuration process and evaluating the proposal for a specific software visualization tool. Our solution facilitates effective communication of quantitative results by helping researchers and developers select and generate the most effective visualizations for each case. This approach opens up new opportunities for research at the intersection of data visualization and variability.

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the “no solution could be found” dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without pre-determining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm’s runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.

Software Product Lines (SPLs) facilitate the development of a complete range of software products through systematic reuse. Reuse involves not only code but also the transfer of knowledge gained from one product to others within the SPL. This transfer includes bug fixing, which, when encountered in one product, affects the entire SPL portfolio. Similarly, feedback obtained from the usage of a single product can inform beyond that product to impact the entire SPL portfolio. Specifically, implicit feedback refers to the automated collection of data on software usage or execution, which allows for the inference of customer preferences and trends. While implicit feedback is commonly used in single-product development, its application in SPLs has not received the same level of attention. This paper promotes the investigation of implicit feedback in SPLs by identifying a set of SPL activities that can benefit the most from it. We validate this usefulness with practitioners using a questionnaire-based approach (n=8). The results provide positive insights into the advantages and practical implications of adopting implicit feedback at the SPL level.

Abstract Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named , (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of and five other state-of-the-art samplers: , , , , and . Our experimental results show only satisfies both scalability and uniformity.

2.3 should be "Method 3: Measure the distance between the theoretical variable probabilities with the empirical variable frequencies in a sample", and the title of Section 2.2.4 should be "Method 4: A statistical goodness-of-fit test that compares the theoretical variable probabilities with the empirical variable frequencies in a sample".

Software Product Lines (SPLs) are about developing a set of different software products that share some common functionality. Feature models are widely used to encode the common and variant parts of an SPL. The number of products encoded in a feature model grows with the number of features. Given n features and no constraints on valid feature combinations, there are 2n possible products. To deal with this complexity, automated mechanisms are used to extract information from feature models, such as features present in every product. A diversity of operations have been developed to model check, test, configure, debug, or compute relevant information by analyzing feature models. Moreover, such operations have been used in scenarios from different domains ranging from operating systems to video analysis optimization. In this tutorial, we go through the different automated analysis operations identifying its usage in the literature. Later present how to implement these operations within the FaMa framework.

Abstract Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

Feature models are widely used to encode the configurations of a software product line in terms of mandatory, optional and exclusive features as well as propositional constraints over the features. Numerous computationally expensive procedures have been developed to model check, test, configure, debug, or compute relevant information of feature models. In this paper we explore the possible improvement of relying on the enumeration of all configurations when performing automated analysis operations. We tackle the challenge of how to scale the existing enumeration techniques by relying on distributed computing. We show that the use of distributed computing techniques might offer practical solutions to previously unsolvable problems and opens new perspectives for the automated analysis of software product lines.

A key problem when developing video processing software is the difficulty to test different input combinations. In this paper, we present VANE, a variability-based testing approach to derive video sequence variants. The ideas of VANE are i) to encode in a variability model what can vary within a video sequence; ii) to exploit the variability model to generate testable configurations; iii) to synthesize variants of video sequences corresponding to configurations. VANE computes T-wise covering sets while optimizing a function over attributes. Also, we present a preliminary validation of the scalability and practicality of VANE in the context of an industrial project involving the test of video processing algorithms.

Orthogonal Variability Model (OVM) is a modelling language for representing variability in Software Product Line Engineering. The automated analysis of OVMs is defined as the computer-aided extraction of information from such models. In this paper, we present FaMa-OVM, which is a pioneer tool for the automated analysis of OVMs. FaMa-OVM is easy to extend or integrate in other tools. It has been developed as part of the FaMa ecosystem enabling the benefits coming from other tools of that ecosystem as FaMaFW and BeTTy.

This workshop will bring together researchers interested in raising the efficiency and the effectiveness of Software Product Line Engineering by applying innovative analysis approaches and formal methods.

In industrial settings, products are rarely developed by one organization alone. Software vendors and suppliers typically maintain their own product lines, which can contribute to a larger (multi) product line. The teams involved often use different approaches and tools to manage the variability of their systems. It is unrealistic to assume that all participating units can use a standardized and prescribed variability modeling technique. The configuration of products based on several models in different notations and with different semantics is not well supported by existing approaches. In this paper we present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototypic implementation based on Web Services. We show the feasibility of the approach and its implementation by using it with two different variability modeling approaches (one feature-based and one decision-oriented approach) on an example derived from industrial experience.

A Feature Model (FM) is a compact representation of all the products of a software product line. The automated extraction of information from FMs is a thriving research topic involving a number of analysis operations, algorithms, paradigms and tools. Implementing these operations is far from trivial and easily leads to errors and defects in analysis solutions. Current testing methods in this context mainly rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses. In this paper, we present a set of relations (so-called metamorphic relations) between input FMs and their set of products and a test data generator relying on them. Given an FM and its known set of products, a set of neighbour FMs together with their corresponding set of products are automatically generated and used for testing different analyses. Complex FMs representing millions of products can be efficiently created applying this process iteratively. The evaluation of our approach using mutation testing as well as real faults and tools reveals that most faults can be automatically detected within a few seconds.

The automation of software product line (SPL) analyses is of growing interest to both practitioners and researchers. In particular, automated analyses of variability models (like feature or decision models) and languages that foster declarative specifications of programs using those models are now common. We note that many of the problems that SPL engineers face are related to configuration problems that have been addressed by the Artificial Intelligence (AI) community. Indeed, the SPL community is using some of their results, e.g., BDD, CSP and SAT solvers.

Variability models, feature diagrams ahead, have become commonplace in the software product lines engineering literature. Whereas ongoing research keeps improving their expressiveness, formalisation and automation, more experience reports on their usage in real projects are needed. This paper describes some challenges encountered during the re-engineering of PloneMeeting, an Open Source software family, into a software product line. The main challenging issues we could observe were (i) the ambiguity originating from implicit information (missing definitions of feature labels and unclear modelling viewpoint), (ii) the necessity of representing spurious features, (iii) the difficulty of making diagrams and constraints resistant to change, and (iv) the risks of using feature attributes to represent large sets of subfeatures. Our study reveals the limitations of current constructs, and calls for both language and methodological improvements. It also suggests further comparative evaluations of modelling alternatives.

FAMA Framework (FAMA FW) is a tool for the automated analysis of variability models (VM). Its main objective is providing an extensible framework where current research on VM automated analysis might be developed and easily integrated into a final product. FAMA FW is built following the SPL paradigm supporting different variability metamodels, reasoners or solvers, analysis questions and reasoner selectors, easing the production of customized VM analysis tools. FAMA FW is written in Java and distributed under LGPL License.

Feature models are widely used to model software product-line (SPL) variability. SPL variants are configured by selecting feature sets that satisfy feature model constraints. Configuration of large feature models can involve multiple stages and participants, which makes it hard to avoid conflicts and errors. New techniques are therefore needed to debug invalid configurations and derive the minimal set of changes to fix flawed configurations. This paper provides three contributions to debugging feature model configurations: (1) we present a technique for transforming a flawed feature model configuration into a Constraint Satisfaction Problem (CSP) and show how a constraint solver can derive the minimal set of feature selection changes to fix an invalid configuration, (2) we show how this diagnosis CSP can automatically resolve conflicts between configuration participant decisions, and (3) we present experiment results that evaluate our technique. These results show that our technique scales to models with over 5,000 features, which is well beyond the size used to validate other automated techniques.