Search

We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach., ACM Transactions on Graphics, 41 (4), ISSN:0730-0301, ISSN:1557-7368

We introduce a novel technique to automatically decompose an input object's volume into a set of parts that can be represented by two opposite height fields. Such decomposition enables the manufacturing of individual parts using two-piece reusable rigid molds. Our decomposition strategy relies on a new energy formulation that utilizes a pre-computed signal on the mesh volume representing the accessibility for a predefined set of extraction directions. Thanks to this novel formulation, our method allows for efficient optimization of a fabrication-aware partitioning of volumes in a completely automatic way. We demonstrate the efficacy of our approach by generating valid volume partitionings for a wide range of complex objects and physically reproducing several of them.

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.

The companies that have adopted the Model-Driven Engineering (MDE) paradigm have the advantage of working at a high level of abstraction. Nevertheless, they have the disadvantage of the lack of tools available to perform bug localization at the model level. In addition, in an MDE context, a bug can be related to different MDE artefacts, such as design-time models, model transformations, or run-time models. Starting the bug localization in the wrong place or with the wrong tool can lead to a result that is unsatisfactory. We evaluate how to apply the existing model-based approaches in order to mitigate the effect of starting the localization in the wrong place. We also take into account that software engineers can refine the results at different stages. In our evaluation, we compare different combinations of the application of bug localization approaches and human refinement. The combination of our approaches plus manual refinement obtains the best results. We performed a statistical analysis to provide evidence of the significance of the results. The conclusions obtained from this evaluation are: humans have to be involved at the right time in the process (or results can even get worse), and artefact-independence can be achieved without worsening the results.

Scientific workflows in High Performance Computing ( HPC ) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS ) that used a Virtual Data Space ( VDS ) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.

Use of third-party libraries is extremely common in application software. The libraries evolve to accommodate new features or mitigate security vulnerabilities, thereby breaking the Application Programming Interface(API) used by the software. Such breaking changes in the libraries may discourage client code from using the new library versions thereby keeping the application vulnerable and not up-to-date. We propose a novel output-oriented program synthesis algorithm to automate API usage adaptations via program transformation. Our aim is not only to rely on the few example human adaptations of the clients from the old library version to the new library version, since this can lead to over-fitting transformation rules. Instead, we also rely on example usages of the new updated library in clients, which provide valuable context for synthesizing and applying the transformation rules. Our tool APIFix provides an automated mechanism to transform application code using the old library versions to code using the new library versions - thereby achieving automated API usage adaptation to fix the effect of breaking changes. Our evaluation shows that the transformation rules inferred by APIFix achieve 98.7% precision and 91.5% recall. By comparing our approach to state-of-the-art program synthesis approaches, we show that our approach significantly reduces over-fitting while synthesizing transformation rules for API usage adaptations.

Rust is a relatively new programming language that targets efficient and safe systems-level applications. It includes a sophisticated type system that allows for provable memory- and thread-safety, and is explicitly designed to take the place of unsafe languages such as C and C++ in the coding ecosystem. There is a large existing C and C++ codebase (many of which have been affected by bugs and security vulnerabilities due to unsafety) that would benefit from being rewritten in Rust to remove an entire class of potential bugs. However, porting these applications to Rust manually is a daunting task. In this paper we investigate the problem of automatically translating C programs into safer Rust programs--that is, Rust programs that improve on the safety guarantees of the original C programs. We conduct an in-depth study into the underlying causes of unsafety in translated programs and the relative impact of fixing each cause. We also describe a novel technique for automatically removing a particular cause of unsafety and evaluate its effectiveness and impact. This paper presents the first empirical study of unsafety in translated Rust programs (as opposed to programs originally written in Rust) and also the first technique for automatically removing causes of unsafety in translated Rust programs.

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts., Proceedings of the ACM on Programming Languages, 5 (OOPSLA), ISSN:2475-1421

How working statically-typed functional programmers write code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers employ. We conducted a grounded theory analysis of 30 programming sessions of practicing statically-typed functional programmers, 15 of which also included a semi-structured interview. The theory we developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory and reasoning strategies, and expressions of intent. We conducted a set of quantitative lab experiments to validate our findings, including that statically-typed functional programmers often iterate between editing types and expressions, that they often run their compiler on code even when they know it will not successfully compile, and that they make textual program edits that reliably signal future edits that they intend to make. Lastly, we outline the implications of our findings for language and tool design. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer populations.

Type-safe languages improve application safety by eliminating whole classes of vulnerabilities–such as buffer overflows–by construction. However, this safety sometimes comes with a performance cost. As a result, many modern type-safe languages provide escape hatches that allow developers to manually bypass them. The relative value of performance to safety and the degree of performance obtained depends upon the application context, including user goals and the hardware upon which the application is to be executed. Since libraries may be used in many different contexts, library developers cannot make safety-performance trade-off decisions appropriate for all cases. Application developers can tune libraries themselves to increase safety or performance, but this requires extra effort and makes libraries less reusable. To address this problem, we present NADER, a Rust development tool that makes applications safer by automatically transforming unsafe code into equivalent safe code according to developer preferences and application context. In end-to-end system evaluations in a given context, NADER automatically reintroduces numerous library bounds checks, in many cases making application code that uses popular Rust libraries safer with no corresponding loss in performance.

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top- k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

Modern static-analysis tools provide powerful and specific insights into codebases. The Linux kernel team, for example, developed Coccinelle, a powerful tool for searching, analyzing, and rewriting C source code; because the Linux kernel contains more than 27 million lines of code, a static-analysis tool is essential both for finding bugs and for making automated changes across its many libraries and modules. Another tool targeted at the C family of languages is Clang scan-build, which comes with many useful analyses and provides an API for programmers to write their own analyses. Like so many things in computer science, the utility of static analysis is self-referential: To write reliable programs, we must also write programs for our programs. But this is no paradox. Static-analysis tools, complex though their theory and practice may be, are what will enable us, and engineers of the future, to overcome this challenge and yield the knowledge and insights that we practitioners deserve.

The Semantic Code team at GitHub builds and operates a suite of technologies that power symbolic code navigation on github.com. We learned that scale is about adoption, user behavior, incremental improvement, and utility. Static analysis in particular is difficult to scale with respect to human behavior; we often think of complex analysis tools working to find potentially problematic patterns in code and then trying to convince the humans to fix them. Our approach took a different tack: use basic analysis techniques to quickly put information that augments our ability to understand programs in front of everyone reading code on GitHub with zero configuration required and almost immediate availability after code changes.

We present a new algorithm for the semi-regular quadrangulation of an input surface, driven by its line features, such as sharp creases. We define a perfectly feature-aligned cross-field and a coarse layout of polygonal-shaped patches where we strictly ensure that all the feature-lines are represented as patch boundaries. To be able to consistently do so, we allow non-quadrilateral patches and T-junctions in the layout; the key is the ability to constrain the layout so that it still admits a globally consistent, T-junction-free, and pure-quad internal tessellation of its patches. This requires the insertion of additional irregular-vertices inside patches, but the regularity of the final-mesh is safeguarded by optimizing for both their number and for their reciprocal alignment. In total, our method guarantees the reproduction of feature-lines by construction, while still producing good quality, isometric, pure-quad, conforming meshes, making it an ideal candidate for CAD models. Moreover, the method is fully automatic, requiring no user intervention, and remarkably reliable, requiring little assumptions on the input mesh, as we demonstrate by batch processing the entire Thingi10K repository, with less than 0.5% of the attempted cases failing to produce a usable mesh.

A future-state architectural strategy designed to support chatbot integration with service delivery systems.

A panoramic view of a popular platform for C program analysis and verification.

Software architectural designs are usually changed over time to support emerging technologies and to adhere to new principles. Architectural migration is an important activity that helps to transform the architectural styles applied during a system’s design with the result of modernising the system. If not performed correctly, this process could lead to potential system failures. This article presents an automated approach to refactoring architectural design and to planning the evolution process. With our solution, the architectural design can be refactored, ensuring that system functionality is preserved. Furthermore, the architectural migration process allows the system to be safely and incrementally transformed. We have evaluated our approach with five real-world software applications. The results prove the effectiveness of our approach and identify factors that impact the performance of architectural verification and migration planning. An interesting finding is that planning algorithms generate migration plans that differ in term of their relative efficiency.

The ad-hoc, heterogeneous process of modern data science typically involves loading, cleaning, and mutating dataset(s) into multiple versions recorded as artifacts by various tools within a single data science workflow. Lineage information, including the source datasets, data transformation programs or scripts, or manual annotations, is rarely captured, making it difficult to infer the relationships between artifacts in a given workflow retrospectively. We demonstrate Relic, a tool to retrospectively infer the lineage of data artifacts generated as a result of typical data science workflows, with an interactive demonstration that allows users to input artifact files and visualize the inferred lineage in a web-based setting.

Over the past two decades, many toolkits for prototyping interactive and ubiquitous electronic devices have been developed. Although their technical specifications are often easy to look up, they vary greatly in terms of design, features and target audience, resulting in very real strengths and weaknesses depending on the intended application. These less technical characteristics are often reported inconsistently, if at all. In this paper we provide a comprehensive survey of interactive and ubiquitous device prototyping toolkits, systematically analysing their characteristics within the framework of a new taxonomy that we present. In addition to the specific characteristics we cover, we introduce a way to evaluate toolkits more holistically, covering user needs such as 'ease of construction' and 'ease of moving from prototype to product' rather than features. We also present results from an online survey which offers new insights on how the surveyed users prioritize these characteristics during prototyping, and what techniques they use to move beyond prototyping. We hope our analysis will be valuable for others in the community who need to build and potentially scale out prototypes as part of their research. We end by identifying gaps that have not yet been addressed by existing offerings and discuss opportunities for future research into electronics prototyping toolkits.

Utility Management in a library is the programmatic tool with the synthetic mental program ability, along with Artificial Intelligence capacities, headed to manage a high volume of books, articles, and assignments, which help to ease the manual significance of librarians. This computerized machine code helps librarians to deal with various databases of the library management system. This framework keeps the records of all the resource details in an optimized manner. It uses a utility management software code with an optimized search classifier that helps to deal with the resource of the library. In this work, the Heuristic Utility Management Algorithm (HUMA) has been used to keep track of resources in the library using mathematical modeling and standardized programmatic computation on tags, which relates the decode scanner to parse the input information. HUMA helps to reduce the manual routine work done by the librarians, and it has been analyzed in this research with prominent survey outcomes based on experimental validation.

Engineering Interactive Computing Systems (EICS) conference; International audience; The goal of software testing is to detect defects with the objective of removing them at a later stage in the development process. Interactive software development follows the User Centered Design approach that promotes continuous involvement of users both at design and evaluation phases. This process is meant to produce usable interactive software by gathering functional and non-functional requirements related to both user needs and context of use. However, taking into account these potentially very-complex-to-implement requirements increases the complexity of the software that is likely, without appropriate methods and tools, to encompass a large number of defects. One of the limitations of UCD approaches is that it provides no guidance on the engineering of the interactive application, which thus usually embeds numerous defects resulting in failures at the origin of user frustrations and performance drops. Even though a classification of interactive application defects has been proposed, interactive application testers remain only superficially supported in detecting them. This paper defines a model-based approach to engineer the testing activity for interactive applications. It proposes a process that bridges the gap between UCD artefacts and interactive software implementation by the production of a dedicated formal model exploited for testing purposes only. The application of the process is demonstrated on an interactive cockpit WIMP application. Finally, threats to validity (capability of the approach to detect defects and to ensure an acceptable coverage testing of the interactive application) are addressed by a longitudinal study on 61 variants of a simple application developed by 61 different developers. ?

Hybrid systems are complex systems where a software controller interacts with a physical environment, usually named a plant, through sensors and actuators. The specification and design of such systems usually rely on the description of both continuous and discrete behaviours. From complex embedded systems to autonomous vehicles, these systems became quite common, including in safety critical domains. However, their formal verification and validation as a whole is still a challenge. To address this challenge, this article contributes to the definition of a reusable and tool supported formal framework handling the design and verification of hybrid system models that integrate both discrete (the controller part) and continuous (the plant part) behaviours. This framework includes the development of a process for defining a class of basic theories and developing domain theories and then the use of these theories to develop a generic model and system-specific models. To realise this framework, we present a formal proof tool chain, based on the Event-B correct-by-construction method and its integrated development environment Rodin, to develop a set of theories, a generic model, proof processes, and the required properties for designing hybrid systems in Event-B. Our approach relies on hybrid automata as basic models for such systems. Discrete and continuous variables model system states and behaviours are given using discrete state changes and continuous evolution following a differential equation. The proposed approach is based on refinement and proof using the Event-B method and the Rodin toolset. Two case studies borrowed from the literature are used to illustrate our approach. An assessment of the proposed approach is provided for evaluating its extensibility, effectiveness, scalability, and usability.

When applying formal verification to a system that interacts with the real world, we must use a model of the environment. This model represents an abstraction of the actual environment, so it is necessarily incomplete and hence presents an issue for system verification. If the actual environment matches the model, then the verification is correct; however, if the environment falls outside the abstraction captured by the model, then we cannot guarantee that the system is well behaved. A solution to this problem consists in exploiting the model of the environment used for statically verifying the system’s behaviour and, if the verification succeeds, using it also for validating the model against the real environment via runtime verification. The article discusses this approach and demonstrates its feasibility by presenting its implementation on top of a framework integrating the Agent Java PathFinder model checker. A high-level Domain Specific Language is used to model the environment in a user-friendly way; the latter is then compiled to trace expressions for both static formal verification and runtime verification. To evaluate our approach, we apply it to two different case studies: an autonomous cruise control system and a simulation of the Mars Curiosity rover.

Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.

The hardware industry is desperate for a modern way to do mechanical design. A new CAD program created for the modern world would lower the barrier to building hardware, decrease the time of development, and usher in a new era of building. The tools used to build with today are supported on the shoulders of giants, but a lot could be done to make them even better. At some point, mechanical CAD lost some of its roots of innovation. Let's dive into a few of the problems with the CAD programs that exist today and see how to make them better.

Focusing on the human element of remote software engineer productivity.

Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs. In SFSS, we identified a set of high-level exception handling strategies based on the QoS performances of different component services and the preferences articled by the service consumers. Multiple recovery plans are generated and evaluated according to the performance of the selected component services, and then we execute the best recovery plan. We assess the overall user dependence (i.e., the service is independent of other services) using the generated plan and the available invocation information of the component services. Due to the experiment results, the given technique enhances the service selection quality by choosing the services that have the highest score and betters the overall system performance. The experiment results indicate the applicability of SFSS and show improved performance in comparison to similar approaches.

IoT platforms enable users to connect various smart devices and online services via reactive apps running on the cloud. These apps, often developed by third-parties, perform simple computations on data triggered by external information sources and actuate the results of computations on external information sinks. Recent research shows that unintended or malicious interactions between the different (even benign) apps of a user can cause severe security and safety risks. These works leverage program analysis techniques to build tools for unveiling unexpected interference across apps for specific use cases. Despite these initial efforts, we are still lacking a semantic framework for understanding interactions between IoT apps. The question of what security policy cross-app interference embodies remains largely unexplored. This article proposes a semantic framework capturing the essence of cross-app interactions in IoT platforms. The framework generalizes and connects syntactic enforcement mechanisms to bisimulation-based notions of security, thus providing a baseline for formulating soundness criteria of these enforcement mechanisms. Specifically, we present a calculus that models the behavioral semantics of a system of apps executing concurrently, and use it to define desirable semantic policies targeting the security and safety of IoT apps. To demonstrate the usefulness of our framework, we define and implement static analyses for enforcing cross-app security and safety, and prove them sound with respect to our semantic conditions. We also leverage real-world apps to validate the practical benefits of our tools based on the proposed enforcement mechanisms.

Quantum Software is a formidable challenge. This is a conse- quence of a rst encounter between software, usually perceived as an engineering discipline, and quantum computing, an outgrowth of two complex and fundamental sciences: physics and computer science. However, Quantum Software is a signi cant creativity op- portunity. This short paper characterizes the unique opportunity, while keeping in mind the great challenge. It also stands as a re- port of a workshop (Q-SE 2021) exactly created to promote under- standing and trigger motivation to exploit this opportunity, while overcoming the challenge. Q-SE 2021 | the 2nd International Workshop on Quantum Software Engineering | was co-located with the 43rd International Conference on Software Engineering (ICSE 2021). Q-SE 2021 was virtually held on June 1 and June 2 of 2021.

Software engineering (SE) researchers and research networks from emerging communities are often not visible in already established Software Engineering venues for a multitude of reasons. This limits the opportunities and mutual bene ts that can arise from collaborations between global and emerging Software Engineer- ing networks. This article focuses on a rst attempt to provide a map of the African software engineering research community with focus on the networks of two big East African Universities. We hope that this very initial mapping e ort will help to raise aware- ness in the international community about the variety of software engineering research in Africa. We formulate some suggestions for making our academic Software Engineering community more inclusive.

In this project, the challenge was to "deploy software faster than the coronavirus spread." In a project with such peculiar characteristics, several factors can influence success, but some clearly stand out: top management support, agility, understanding and commitment of the project team, and the technology used. Conventional development approaches and technologies would simply not be able to meet the requirements promptly.

© ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Software Engineering and Methodology, http://dx.doi.org/10.1145/10.1145/3428076, Models are the central assets in model-driven engineering (MDE), as they are actively used in all phases of software development. Models are built using metamodel-based languages, and so objects in models are typed by a metamodel class. This typing is static, established at creation time, and cannot be changed later. Therefore, objects in MDE are closed and fixed with respect to the class they conform to, the fields they have, and the well-formedness constraints they must comply with. This hampers many MDE activities, like the reuse of model-related artefacts such as transformations, the opportunistic or dynamic combination of metamodels, or the dynamic reconfiguration of models. To alleviate this rigidity, we propose making model objects open so that they can acquire or drop so-called facets. These contribute with a type, fields and constraints to the objects holding them. Facets are defined by regular metamodels, hence being a lightweight extension of standard metamodelling. Facet metamodels may declare usage interfaces, as well as laws that govern the assignment of facets to objects (or classes). This article describes our proposal, reporting on a theory, analysis techniques, and an implementation. The benefits of the approach are validated on the basis of five case studies dealing with annotation models, transformation reuse, multi-view modelling, multi-level modelling, and language product lines, Work partially funded by the R&D programme of the Madrid Region (project FORTE, S2018/TCS-4314) and the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00)

The design and development process for internet of things (IoT) applications is more complicated than that for desktop, mobile, or web applications. First, IoT applications require both software and hardware to work together across many different types of nodes with different capabilities under different conditions. Second, IoT application development involves different types of software engineers such as desktop, web, embedded, and mobile to work together. Furthermore, non-software engineering personnel such as business analysts are also involved in the design process. In addition to the complexity of having multiple software engineering specialists cooperating to merge different hardware and software components together, the development process requires different software and hardware stacks to be integrated together (e.g., different stacks from different companies such as Microsoft Azure and IBM Bluemix). Due to the above complexities, non-functional requirements (such as security and privacy, which are highly important in the context of the IoT) tend to be ignored or treated as though they are less important in the IoT application development process. This article reviews techniques, methods, and tools to support security and privacy requirements in existing non-IoT application designs, enabling their use and integration into IoT applications. This article primarily focuses on design notations, models, and languages that facilitate capturing non-functional requirements (i.e., security and privacy). Our goal is not only to analyse, compare, and consolidate the empirical research but also to appreciate their findings and discuss their applicability for the IoT.

Spreadsheet systems are by far the most popular platform for data exploration on the planet, supporting millions of rows of data. However, exploring spreadsheets that are this large via operations such as scrolling or issuing formulae can be overwhelming and error-prone. Users easily lose context and suffer from cognitive and mechanical burdens while issuing formulae on data spanning multiple screens. To address these challenges, we introduce dynamic hierarchical overviews that are embedded alongside spreadsheets. Users can employ this overview to explore the data at various granularities, zooming in and out of the spreadsheet. They can issue formulae over data subsets without cumbersome scrolling or range selection, enabling users to gain a high or low-level perspective of the spreadsheet. An implementation of our dynamic hierarchical overview, NOAH, integrated within DataSpread, preserves spreadsheet semantics and look and feel, while introducing such enhancements. Our user studies demonstrate that NOAH makes it more intuitive, easier, and faster to navigate spreadsheet data compared to traditional spreadsheets like Microsoft Excel and spreadsheet plug-ins like Pivot Table, for a variety of exploration tasks; participants made fewer mistakes in NOAH while being faster in completing the tasks.

Creating modern software inevitably requires using application programming interfaces (APIs). While software developers can sometimes use APIs by simply copying and pasting code examples, a lack of robust knowledge of how an API works can lead to defects, complicate software maintenance, and limit what someone can express with an API. Prior work has uncovered the many ways that API documentation fails to be helpful, though rarely describes precisely why. We present a theory of robust API knowledge that attempts to explain why, arguing that effective understanding and use of APIs depends on three components of knowledge: (1) the domain concepts the API models along with terminology, (2) the usage patterns of APIs along with rationale, and (3) facts about an API’s execution to support reasoning about its runtime behavior. We derive five hypotheses from this theory and present a study to test them. Our study investigated the effect of having access to these components of knowledge, finding that while learners requested these three components of knowledge when they were not available, whether the knowledge helped the learner use or understand the API depended on the tasks and likely the relevance and quality of the specific information provided. The theory and our evidence in support of its claims have implications for what content API documentation, tutorials, and instruction should contain and the importance of giving the right information at the right time, as well as what information API tools should compute, and even how APIs should be designed. Future work is necessary to both further test and refine the theory, as well as exploit its ideas for better instructional design.

An approach to reproducibility problems related to porting software across machines and compilers.

The paper Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009 received the 2020 SIGEVO Impact Award for ten-year impact, which was announce at the ACM GECCO 2020 conference. The work compares the performance of 31 algorithms that had been benchmarked with the Comparing Continuous Optimizer platform (COCO) for the Black-Box-Optimization Benchmarking (BBOB) GECCO workshop in 2009.

Assertions are widely used for functional validation as well as coverage analysis for both software and hardware designs. Assertions enable runtime error detection as well as faster localization of errors. While there is a vast literature on both software and hardware assertions for monitoring functional scenarios, there is limited effort in utilizing assertions to monitor System-on-Chip (SoC) security vulnerabilities. We have identified common SoC security vulnerabilities and defined several classes of assertions to enable runtime checking of security vulnerabilities. A major challenge in assertion-based validation is how to activate the security assertions to ensure that they are valid. While existing test generation using model checking is promising, it cannot generate directed tests for large designs due to state space explosion. We propose an automated and scalable mechanism to generate directed tests using a combination of symbolic execution and concrete simulation of RTL models. Experimental results on diverse benchmarks demonstrate that the directed tests are able to activate security assertions non-vacuously.

With mobile and cloud computing, the software industry has witnessed a shift from customer-specific systems to a mass-market scenario. Integrating the "voice of the customer" is a critical success factor for mass-market products, but remains a challenge given the heterogeneity and distribution of their users. Classical requirements engineering methods rely on close interactions to gather user requirements and feedback regarding product features. However, they are hardly applicable in mass market scenarios, and new methods are required to assist product managers in designing their products while taking into consideration the opinion of the mass. In this paper, we argue for using market research techniques as preference-based approach in software product management. Following method engineering guidelines, we develop a method component 1 that refines conjoint analysis for the use in software product management. We present the meta-model and procedure and demonstrate it in a study on secure cloud storage services. We evaluate the feasibility, usefulness and ease of use of method component with experts in the RE domain. We contribute to mass-market software product management domain through a method component that builds on detailed understanding of user preferences for successful product design.

A significant proportion of classes in modern software introduce or use object protocols, prescriptions on the temporal orderings of method calls on objects. This article studies search-based test generation techniques that aim to exploit a particular abstraction of object protocols (enabledness preserving abstractions (EPAs)) to find failures. We define coverage criteria over an extension of EPAs that includes abnormal method termination and define a search-based test case generation technique aimed at achieving high coverage. Results suggest that the proposed case generation technique with a fitness function that aims at combined structural and extended EPA coverage can provide better failure-detection capabilities not only for protocol failures but also for general failures when compared to random testing and search-based test generation for standard structural coverage.

Models in Model-driven Engineering are primary development artifacts that are heavily edited in all stages of software development and that can become temporarily inconsistent during editing. In general, there are many alternatives to resolve an inconsistency, and which one is the most suitable depends on a variety of factors. As also proposed by recent approaches to model repair, it is reasonable to leave the actual choice and approval of a repair alternative to the discretion of the developer. Model repair tools can support developers by proposing a list of the most promising repairs. Such repair recommendations will be only accepted in practice if the generated proposals are plausible and understandable, and if the set as a whole is manageable. Current approaches, which mostly focus on exhaustive search strategies, exploring all possible model repairs without considering the intention of historic changes, fail in meeting these requirements. In this article, we present a new approach to generate repair proposals that aims at inconsistencies that have been introduced by past incomplete edit steps that can be located in the version history of a model. Such an incomplete edit step is either undone or it is extended to a full execution of a consistency-preserving edit operation. The history-based analysis of inconsistencies as well as the generation of repair recommendations are fully automated, and all interactive selection steps are supported by our repair tool called R E V ISION . We evaluate our approach using histories of real-world models obtained from popular open-source modeling projects hosted in the Eclipse Git repository, including the evolution of the entire UML meta-model. Our experimental results confirm our hypothesis that most of the inconsistencies, namely, 93.4, can be resolved by complementing incomplete edits. 92.6% of the generated repair proposals are relevant in the sense that their effect can be observed in the models’ histories. 94.9% of the relevant repair proposals are ranked at the topmost position.

System emulation and firmware re-hosting have become popular techniques to answer various security and performance related questions, such as determining whether a firmware contain security vulnerabilities or meet timing requirements when run on a specific hardware platform. While this motivation for emulation and binary analysis has previously been explored and reported, starting to either work or research in the field is difficult. To this end, we provide a comprehensive guide for the practitioner or system emulation researcher. We layout common challenges faced during firmware re-hosting, explaining successive steps and surveying common tools used to overcome these challenges. We provide classification techniques on five different axes, including emulator methods, system type, fidelity, emulator purpose, and control. These classifications and comparison criteria enable the practitioner to determine the appropriate tool for emulation. We use our classifications to categorize popular works in the field and present 28 common challenges faced when creating, emulating, and analyzing a system from obtaining firmwares to post emulation analysis.

More than two decades ago, researchers started to mine the data stored in software repositories to help software developers in making informed decisions for developing and testing software systems. Bug prediction was one of the most promising and popular research directions that uses the data stored in software repositories to predict the bug-proneness or number of bugs in source files. On that topic and as part of Emanuel's PhD studies, we submitted a paper with the title Comparing fine-grained source code changes and code churn for bug prediction [8] to the 8th Working Conference on Mining Software Engineering, held 2011 in beautiful Honolulu, Hawaii. Ten years later, it got selected as one of the finalists to receive the MSR 2021 Most Influential Paper Award. In the following, we provide a retrospective on our work, describing the road to publishing this paper, its impact in the field of bug prediction, and the road ahead.

Automated program repair is an emerging area that attempts to patch software errors and vulnerabilities. In this article, we formulate and study a problem related to automated repair, namely automated patch transplantation. A patch for an error in a donor program is automatically adapted and inserted into a “similar” target program. We observe that despite standard procedures for vulnerability disclosures and publishing of patches, many un-patched occurrences remain in the wild. One of the main reasons is the fact that various implementations of the same functionality may exist and, hence, published patches need to be modified and adapted. In this article, we therefore propose and implement a workflow for transplanting patches. Our approach centers on identifying patch insertion points, as well as namespaces translation across programs via symbolic execution. Experimental results to eliminate five classes of errors highlight our ability to fix recurring vulnerabilities across various programs through transplantation. We report that in 20 of 24 fixing tasks involving eight application subjects mostly involving file processing programs, we successfully transplanted the patch and validated the transplantation through differential testing. Since the publication of patches make an un-patched implementation more vulnerable, our proposed techniques should serve a long-standing need in practice.

A field study examines technological advances that have created versatile software ecosystems to develop and deploy microservices.

Building and programming tiny battery-free energy harvesting embedded computer systems is hard for the average maker because of the lack of tools, hard to comprehend programming models, and frequent power failures. With the high ecologic cost of equipping the next trillion embedded devices with batteries, it is critical to equip the makers, hobbyists, and novice embedded systems programmers with easy-to-use tools supporting battery-free energy harvesting application development. This way, makers can create untethered embedded systems that are not plugged into the wall, the desktop, or even a battery, providing numerous new applications and allowing for a more sustainable vision of ubiquitous computing. In this paper, we present BFree, a system that makes it possible for makers, hobbyists, and novice embedded programmers to develop battery-free applications using Python programming language and widely available hobbyist maker platforms. BFree provides energy harvesting hardware and a power failure resilient version of Python, with durable libraries that enable common coding practice and off the shelf sensors. We develop demonstration applications, benchmark BFree against battery-powered approaches, and evaluate our system in a user study. This work enables makers to engage with a future of ubiquitous computing that is useful, long-term, and environmentally responsible.