[Motivation] Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such languages can lead to errors that are challenging to identify early with traditional static analysis tools. Recent progress in deep neural networks has led to increased use of neural bug detectors. In statically typed languages, a type checker is integrated into the compiler and thus taken into consideration when the neural bug detector is designed for these languages. [Problem] However, prior studies overlook this aspect during the training and testing of neural bug detectors for dynamically typed languages. When an optional type checker is used, assessing existing neural bug detectors on bugs easily detectable by type checkers may impact their performance estimation. Moreover, including these bugs in the training set of neural bug detectors can shift their detection focus toward the wrong type of bugs. [Contribution] We explore the impact of type checking on various neural bug detectors for variable misuse bugs, a common type targeted by neural bug detectors. Existing synthetic and real-world datasets are type-checked to evaluate the prevalence of type-related bugs. Then, we investigate how type-related bugs influence the training and testing of the neural bug detectors. [Findings] Our findings indicate that existing bug detection datasets contain a significant proportion of type-related bugs. Building on this insight, we discover integrating the neural bug detector with a type checker can be beneficial, especially when the code is annotated with types. Further investigation reveals neural bug detectors perform better on type-related bugs than other bugs. Moreover, removing type-related bugs from the training data helps improve neural bug detectors' ability to identify bugs beyond the scope of type checkers.

Recently, large language models (LLMs) have achieved widespread application across various fields. Despite their impressive capabilities, LLMs suffer from a lack of structured reasoning ability, particularly for complex tasks requiring domain-specific best practices, which are often unavailable in the training data. Although multi-step prompting methods incorporating human best practices, such as chain-of-thought and tree-of-thought, have gained popularity, they lack a general mechanism to control LLM behavior. In this paper, we propose SHERPA, a model-driven framework to improve the LLM performance on complex tasks by explicitly incorporating domain-specific best practices into hierarchical state machines. By structuring the LLM execution processes using state machines, SHERPA enables more fine-grained control over their behavior via rules or decisions driven by machine learning-based approaches, including LLMs. We show that SHERPA is applicable to a wide variety of tasks-specifically, code generation, class name generation, and question answering-replicating previously proposed approaches while further improving the performance. We demonstrate the effectiveness of SHERPA for the aforementioned tasks using various LLMs. Our systematic evaluation compares different state machine configurations against baseline approaches without state machines. Results show that integrating well-designed state machines significantly improves the quality of LLM outputs, and is particularly beneficial for complex tasks with well-established human best practices but lacking data used for training LLMs.

Graph model generation from natural language description is an important task with many applications in software engineering. With the rise of large language models (LLMs), there is a growing interest in using LLMs for graph model generation. Nevertheless, LLM-based graph model generation typically produces partially correct models that suffer from three main issues: (1) syntax violations: the generated model may not adhere to the syntax defined by its metamodel, (2) constraint inconsistencies: the structure of the model might not conform to some domain-specific constraints, and (3) inaccuracy: due to the inherent uncertainty in LLMs, the models can include inaccurate, hallucinated elements. While the first issue is often addressed through techniques such as constraint decoding or filtering, the latter two remain largely unaddressed. Motivated by recent self-consistency approaches in LLMs, we propose a novel abstraction-concretization framework that enhances the consistency and quality of generated graph models by considering multiple outputs from an LLM. Our approach first constructs a probabilistic partial model that aggregates all candidate outputs and then refines this partial model into the most appropriate concrete model that satisfies all constraints. We evaluate our framework on several popular open-source and closed-source LLMs using diverse datasets for model generation tasks. The results demonstrate that our approach significantly improves both the consistency and quality of the generated graph models.

Requirements over strings, commonly represented using natural language (NL), are particularly relevant for software systems due to their heavy reliance on string data manipulation. While individual requirements can usually be analyzed manually, verifying properties (e.g., satisfiability) over sets of NL requirements is particularly challenging. Formal approaches (e.g., SMT solvers) may efficiently verify such properties, but are known to have theoretical limitations. Additionally, the translation of NL requirements into formal constraints typically requires significant manual effort. Recently, large language models (LLMs) have emerged as an alternative approach for formal reasoning tasks, but their effectiveness in verifying requirements over strings is less studied. In this paper, we introduce a hybrid approach that verifies the satisfiability of NL requirements over strings by using LLMs (1) to derive a satisfiability outcome (and a consistent string, if possible), and (2) to generate declarative (i.e., SMT) and imperative (i.e., Python) checkers, used to validate the correctness of (1). In our experiments, we assess the performance of four LLMs. Results show that LLMs effectively translate natural language into checkers, even achieving perfect testing accuracy for Python-based checkers. These checkers substantially help LLMs in generating a consistent string and accurately identifying unsatisfiable requirements, leading to more than doubled generation success rate and F1-score in certain cases compared to baselines without generated checkers.

To succeed with the development of modern and complex systems (e.g., aircrafts or production systems), organizations must have the agility to adapt faster to constantly evolving requirements in order to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, suppliers, and partners. However, stakeholders do not have sufficiently explicit and systematic support for global decision making, considering the vast decision space and complex inter-relationships. This decision space is characterized by increasing yet inadequately represented variability and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. As a result, one team's design decisions may impose too restrictive requirements on another team. In this paper, we evaluate our understanding of global decision making in the context of complex system development based on a conceptual model which explicitly represents and manages decision spaces including variability and impacts. We have conducted our evaluation by means of an exploratory case study where we interviewed domain experts with an average of 20 years of experience in complex system industries and report the key findings and remaining challenges. In the future, we aim at providing explicit and systematic tool-supported approaches for global decision making support for complex systems.

Software systems evolve more and more in complex and changing environments, often requiring runtime adaptation to best deliver their services. When self-adaptation is the main concern of the system, a manual implementation of the underlying feedback loop and trade-off analysis may be desirable. However, the required expertise and substantial development effort make such implementations prohibitively difficult when it is only a secondary concern for the given domain. In this paper, we present ASOS, a metalanguage abstracting the runtime adaptation concern of a given domain in the behavioral semantics of a domain-specific language (DSL), freeing the language user from implementing it from scratch for each system in the domain. We demonstrate our approach on RobLANG, a procedural DSL for robotics, where we abstract a recurrent energy-saving behavior depending on the context. We provide formal semantics for ASOS and pave the way for checking properties such as determinism, completeness, and termination of the resulting self-adaptable language. We provide first results on the performance of our approach compared to a manual implementation of this self-adaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.

To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.

In domain modelling, practitioners manually transform informal requirements written in natural language (problem descriptions) to more concise and analyzable domain models expressed with class diagrams. With automated domain modelling support using existing approaches, manual modifications may still be required in extracted domain models and problem descriptions to make them more accurate and concise. For example, educators teaching software engineering courses at universities usually use an incremental approach to build modelling exercises to restrict students in using intended modelling patterns. These modifications result in the evolution of domain modelling exercises over time. To assist practitioners in this evolution, a synergy between interactive support and automated domain modelling is required. In this paper, we propose a bot-assisted approach to allow practitioners perform domain modelling quickly and interactively. Furthermore, we provide an incremental learning strategy empowered by machine learning to improve the accuracy of the bot's suggestions and extracted domain models by analyzing practitioners' decisions over time. We evaluate the performance of our bot using test problem descriptions which shows that practitioners can expect to get useful support from the bot when applied to exercises of similar size and complexity, with precision, recall, and F2 scores over 85%. Finally, we evaluate our incremental learning strategy where we observe a reduction in the required manual modifications by 70% and an improvement of F2 scores of extracted domain models by 4.2% when using our proposed approach and learning strategy together.

Reviewer recommendation systems are used to suggest community members to review change requests. Like several other recommendation systems, it is customary to evaluate recommendations using held out historical data. While history-based evaluation makes pragmatic use of available data, historical records may be: (1) overly optimistic, since past assignees may have been suboptimal choices for the task at hand; or (2) overly pessimistic, since "incorrect" recommendations may have been equal (or even better) choices.In this paper, we empirically evaluate the extent to which historical data is an appropriate benchmark for reviewer recommendation systems. We replicate the CHREV and WLRREC approaches and apply them to 9,679 reviews from the GERRIT open source community. We then assess the recommendations with members of the GERRIT reviewing community using quantitative methods (personalized questionnaires about their comfort level with tasks) and qualitative methods (semi-structured interviews).We find that history-based evaluation is far more pessimistic than optimistic in the context of GERRIT review recommendations. Indeed, while 86% of those who had been assigned to a review in the past felt comfortable handling the review, 74% of those labelled as incorrect recommendations also felt that they would have been comfortable reviewing the changes. This indicates that, on the one hand, when reviewer recommendation systems recommend the past assignee, they should indeed be considered correct. Yet, on the other hand, recommendations labelled as incorrect because they do not match the past assignee may have been correct as well.Our results suggest that current reviewer recommendation evaluations do not always model the reality of software development. Future studies may benefit from looking beyond repository data to gain a clearer understanding of the practical value of proposed recommendations.

Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Modellers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both time-consuming and error-prone. These issues are further aggravated when problem descriptions are long, which makes it hard to trace modelling decisions from domain models to problem descriptions or vice-versa leading to completeness and conciseness issues. Automated support for tracing domain modelling decisions in both directions is thus advantageous. In this paper, we propose an automated approach that uses artificial intelligence techniques to extract domain models along with their trace links. We present a traceability information model to enable traceability of modelling decisions in both directions and provide its proof-of-concept in the form of a tool. The evaluation on a set of unseen problem descriptions shows that our approach is promising with an overall median F2 score of 82.04%. We conduct an exploratory user study to assess the benefits and limitations of our approach and present the lessons learned from this study.

In the initial phases of the software development cycle, domain modelling is typically performed to transform informal requirements expressed in natural language into concise and analyzable domain models. These models capture the key concepts of an application domain and their relationships in the form of class diagrams. Building domain models manually is often a time-consuming and labor-intensive task. The current approaches which aim to extract domain models automatically, are inadequate in providing insights into the modelling decisions taken by extractor systems. This inhibits modellers to quickly confirm the completeness and conciseness of extracted domain models. To address these challenges, we present DoMoBOT, a domain modelling bot that uses a traceability knowledge graph to enable traceability of modelling decisions from extracted domain model elements to requirements and vice-versa. In this tool demo paper, we showcase how the implementation and architecture of DoMoBOT facilitate modellers to extract domain models and gain insights into the modelling decisions taken by our bot.

Over recent years, self-adaptation has become a major concern for software systems that evolve in changing environments. While expert developers may choose a manual implementation when self-adaptation is the primary concern, self-adaptation should be abstracted for non-expert developers or when it is a secondary concern. We present SEALS, a framework for building self-adaptive virtual machines for domain-specific languages. This framework provides first-class entities for the language engineer to promote domain-specific feedback loops in the definition of the DSL operational semantics. In particular, the framework supports the definition of (i) the abstract syntax and the semantics of the language as well as the correctness envelope defining the acceptable semantics for a domain concept, (ii) the feedback loop and associated trade-off reasoning, and (iii) the adaptations and the predictive model of their impact on the trade-off. We use this framework to build three languages with self-adaptive virtual machines and discuss the relevance of the abstractions, effectiveness of correctness envelopes, and compare their code size and performance results to their manually implemented counterparts. We show that the framework provides suitable abstractions for the implementation of self-adaptive operational semantics while introducing little performance overhead compared to a manual implementation.

Software failures that demonstrate violations of human values can result in financial losses, reputation damages and social implications. Therefore, integrating human values into software is vital to satisfy stakeholder needs. However, developing methodological approaches that allow systematic integration of human values throughout the software development life cycle is an open challenge. This paper proposes the Continual Value(s) Assessment (CVA) framework that uses extended goal and feature modeling techniques to support systematic integration, tracing and evaluation of human values in software systems. The CVA framework prescribes (i) brainstorming of value implications of system features based on conventional system artefacts and (ii) the expansion of the existing set of system features to better serve stakeholder values expectations. In a pilot study, we use an emergency alarm system for the elderly to demonstrate the feasibility of the framework. We further discuss the challenges we faced while applying the framework and present the lessons learned from the pilot study.

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

To describe the characteristics of complex software systems, model-driven engineering (MDE) advocates the use of different modeling languages and multiple views. These models are typically organized in a nested structure or grouped according to some criteria. A modeller needs to navigate this structure to understand and modify the system under development. This paper introduces a navigation bar that visually indicates to the modeller the place of a model in that structure. Furthermore, a generic navigation mechanism facilitates navigation within a model and from one model to other linked models potentially expressed in a different language. We present a navigation metamodel that a language designer can use to enhance a modelling language at the metamodel level with our generic navigation capabilities.

Collaborative modelling has become a necessity when developing a complex system or in a team of modellers with a diverse set of expertise. Textual notations have a long history in software engineering because of their fast editing style, simple usage, and scalability. Therefore, we propose a novel collaborative modelling framework for the graphical User Requirements Notation (URN) which we call tColab. It uses the text-based TGRL (Textual Goal-oriented Requirement Language) to build URN goal models and then automatically generates corresponding graphical models. This framework is based on the architecture of Eclipse Che and Theia. On one side, Theia provides support for LSP (Language Server Protocol) so that textual models can be built and their corresponding graphical models can be generated in a browser IDE (Integrated Development Environment). On the other hand, Eclipse Che adds support for collaboration where multiple modellers can contribute to building the textual models in an online collaborative manner. This initiative aims to replace the jUCMNAV tool, which is the most comprehensive URN modelling tool to date but only supports a single user.

Continuous Integration (CI) is a widely used practice where code changes are automatically built and tested to check for regression as they appear in the Version Control System (VCS). CI services allow users to customize phases, which define the sequential steps of build jobs that are triggered by changes to the project. While past work has made important observations about the adoption and usage of CI, little is known about patterns of reuse in CI specifications. Should reuse be common in CI specifications, we envision that a tool could guide developers through the generation of CI specifications by offering suggestions based on popular sequences of phases and commands. To assess the feasibility of such a tool, we perform an empirical analysis of the use of different phases and commands in a curated sample of 913 CI specifications for Java-based projects that use Travis CI-one of the most popular public CI service providers. First, we observe that five of nine phases are used in 18%-75% of the projects. Second, for the five most popular phases, we apply association rule mining to discover frequent phase, command, and command category usage patterns. Unfortunately, we observe that the association rules lack sufficient support, confidence, or lift values to be considered statistically significantly interesting. Our findings suggest that the usage of phases and commands in Travis CI specifications are broad and diverse. Hence, we cannot provide suggestions for Java-based projects as we had envisioned.

Many modern systems are built to be in use for a long time, sometimes spanning several decades. Furthermore, considering the cost of replacing an existing system, existing systems are usually adapted to evolving requirements, some of which may be anticipated. Such systems need to be specified and analyzed in terms of whether the changes introduced into the system still address evolving requirements and continue to satisfy the needs of its stakeholders. Recently, the User Requirements Notation (URN) - a requirements engineering notation standardized by the International Telecommunication Union for the elicitation, specification, and analysis of integrated goal and scenario models - has been extended with support for the modeling and analysis of evolving requirements models by capturing a comprehensive set of changes to a URN model as first-class model concepts in URN. The extension is called TimedURN and this paper builds on TimedURN to analyze and visualize not just an individual time point in the past, present, or future but a time range consisting of a set of time points. Various visualization options are discussed, including their proof-of-concept implementation in the jUCMNav requirements engineering tool.

The User Requirements Notation (URN) supports the elicitation, specification, and analysis of integrated goal and scenario models. The analysis of the goal and scenario models focuses on one snapshot in time and does not allow the model to change over time. While several models may be created that represent different stages of a system, managing several, slightly different model copies is a space-consuming, time-consuming, and error-prone task that makes it difficult to maintain consistency across the model copies. This paper introduces TimedURN, an extension of the URN standard, which enables the modeling and analysis of a comprehensive set of changes to a goal and scenario model over time. The changes to the model are captured in one base model, which eases system evolution. The metamodel for TimedURN is presented and it is argued that it can also be applied to other modeling languages. Furthermore, the usefulness of TimedURN is illustrated with an example from the sustainability domain and the comprehensiveness of the supported types of changes is assessed.

The User Requirements Notation is a standard published by the International Telecommunication Union that contains two complementary notations for goal and scenario/workflow modeling. Use Case Maps (UCM) - the workflow notation - focuses on the causal relationships of the steps in a workflow without requiring the specification of detailed message exchanges and data. A UCM model captures the interactions between actors and the system and typically integrates several use cases into a combined system view. This results in a high-level description of the system and its end-to-end usage scenarios. At the UCM level, scenario definitions create a regression test suite for the UCM model. This paper investigates the transformation of such workflow models into end-to-end acceptance test cases that can be automated with the JUnit testing framework. For that purpose, the UCM model is enriched with (i) input data types and expected results, (ii) a code-level description of system behavior as needed for the workflow, and (iii) testing logic including assertions. Based on this specification, the proposed approach uses boundary value analysis of the input data and Myer's test selection heuristics to determine a set of test cases for the described workflow. Coverage criteria may be specified at the UCM model level. Results from a case study of a small data management system indicate a reduction of the number of lines of code that need to be specified in the workflow model vs. the test implementation by an order of magnitude.

Summary Concern‐oriented reuse (CORE) proposes the concern as a new unit of model‐based reuse encapsulating software artefacts pertaining to a domain of interest that span multiple development phases and levels of abstraction. With CORE, a concern encapsulates multiple reusable features, while allowing its generic models to be customized to problem‐specific contexts. We report on our experience of designing a family of crisis management systems (CMS) with the help of reusable concern libraries. The collected metrics show a considerable amount of reuse in our CMS design. The study provides encouraging evidence that CORE's vision to create large‐scale, generic and reusable entities that are expressed with the most appropriate modelling formalisms at the right level of abstraction is feasible. We present our experience in the design of the CMS and elaborate on the advantages as well as the efforts required to adopt CORE in an industrial setting. Copyright © 2016 John Wiley & Sons, Ltd.

Concern-Oriented Reuse (CORE) proposes a new way of structuring model-driven software development, where models of the system are modularized by domains of abstraction within units of reuse called concerns. Within a CORE concern, models are further decomposed and modularized by features. This paper extends CORE with a technique that enables developers of high-level concerns to reuse lower-level concerns without unnecessarily committing to a specific feature selection. The developer can select the functionality that is minimally needed to continue development, and reexpose relevant alternative lower-level features of the reused concern in the reusing concern's interface. This effectively delays decision making about alternative functionality until the higher-level reuse context, where more detailed requirements are known and further decisions can be made. The paper describes the algorithms for composing the variation (i.e., feature and impact models), customization, and usage interfaces of a concern, as well as the concern's realization models and finally an entire concern hierarchy, as is necessary to support delayed decision making in CORE.

The User Requirements Notation (URN) is an international requirements engineering standard published by the International Telecommunication Union. URN supports goal-oriented and scenario-based modeling and analysis. jUCMNav is an open-source, Eclipse-based modeling tool for URN. This tool demonstration focuses on recent extensions to jUCMNav that have incorporated feature models into a URN-based modeling and reasoning framework. Feature modeling is a well-establishing technique for capturing commonalities and variabilities of Software Product Lines. Combined with URN, it is possible to reason about the impact of feature configurations on stakeholder goals and system qualities, thus helping to identify the most appropriate features for a stakeholder. Furthermore, coordinated feature and goal model reasoning is fundamental to Concern-Driven Development, where concerns are defined with a three-part variation, customization, and usage interface. As the variation interface is described with feature and goal models, it is now possible with jUCMNav to define and reason about a concern's variation interface, which is a prerequisite for composing multiple concerns based on their three-part interfaces.

Reuse is a powerful tool for improving the productivity of software development. The paper puts forward arguments in favor of generic requirements reuse rooted in the vision that effectiveness requires a focus on coordinated composition of reusable artifacts across the whole software development life cycle. A survey of publications on requirements reuse from the International Requirements Engineering (RE) Conference series determines the research landscape in this area over the last twenty years, assessing the hypothesis that there is no or little research reported at RE about generic reuse of requirements models that spans the software development life cycle. The paper then outlines, for the RE community, a research agenda associated with the presented vision for such an approach to requirements reuse that builds on concern-orientation, i.e., the ability to modularize and compose important requirements concerns throughout the software development life cycle, and model-engineering principles. In addition, early research results are briefly presented that illustrate favorably the feasibility of such an approach.

i* is a goal-oriented and agent-oriented modeling framework that focuses on the analysis of intentional and strategic relationships among actors. In this mini-tutorial, we highlight a number of recent applications in practical industrial and business settings.

Transport Canada is reviewing its Aviation Security regulations in a multi-year modernization process. As part of this review, consideration is given to transitioning regulations where appropriate from a prescriptive style to an outcome-based style. This raises new technical and cultural challenges related to how to measure compliance. This paper reports on a novel approach used to model regulations with the Goal-oriented Requirement Language, augmented with qualitative indicators. These models are used to guide the generation of questions for inspection activities, enable a flexible conversion of real-world data into goal satisfaction levels, and facilitate compliance analysis. A new propagation mechanism enables the evaluation of the compliance level of an organization. This outcome-based approach is expected to help get a more precise understanding of who complies with what, while highlighting opportunities for improving existing regulatory elements.

We show how a technology called Umple can be used to improve teaching UML and modeling. Umple allows UML to be viewed both textually and graphically, with updates to one view reflected in the other. It allows UML concepts to be added to programming languages, plus web-based code generation from UML to those languages. We have used Umple in student laboratories and assignments for two years, and also live in the classroom. In a survey, students showed enthusiasm about Umple, and indicated they believe it helps them understand UML better. Improvements in their grades also support our approach.

In organizations, a gap commonly exists between business goals and business processes. While several approaches provide modeling solutions in each of these two areas, their relationships are often not defined well enough to be used in the software development process. This paper aims to better fill this gap through the introduction of a pattern-based framework that helps construct business processes from organization goals while maintaining traceability relationships between the two. How to extract patterns, which are composed of goal templates, process templates, and their relationships, is briefly presented. The framework, which includes a collection of patterns for a particular domain, is formalized as a profile of the User Requirements Notation, a standard modeling language that supports goals, scenarios, and links between them. A method for the use of such framework is defined and then illustrated through a case study involving an adverse event management system that targets the improvement of patient safety in healthcare organizations.

In this article, we introduce the application of rigorous analysis procedures to goal models to provide several benefits beyond the initial act of modeling. Such analysis can allow modelers to assess the satisfaction of goals, facilitate evaluation of high-level design alternatives, help analysts decide on the high-level requirements and design of the system, test the sanity of a model, and support communication and learning. The analysis of goal models can be done in very different ways depending on the nature of the model and the purpose of the analysis. In our work, we use the Goal-oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN). URN, a new Recommendation of the International Telecommunications Union, provides the first standard goal-oriented language. Using GRL, we develop an approach to analysis that can be done by evaluating qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., goals and tasks) composing the model. Initial satisfaction levels for some of the intentional elements are provided in a strategy and then propagated to the other intentional elements of the model through the various links that connect them. The results allow for an assessment of the relative effectiveness of design alternatives at the requirements level. Although no specific propagation algorithm is imposed in the URN standard, different criteria for defining evaluation mechanisms are described. We provide three algorithms (quantitative, qualitative, and hybrid) as examples, which satisfy the constraints imposed by the standard. These algorithms have been implemented in the open-source jUCMNav tool, an Eclipse-based editor for URN models. The algorithms are presented and compared with the help of a telecommunication system example. © 2010 Wiley Periodicals, Inc.

In November 2008, the User Requirements Notation (URN) was approved as a standard by the International Telecommunication Union (ITU-T). jUCMNav is the most comprehensive tool available to date that supports the definition, analysis, transformation, and management of URN requirements engineering models. URN is the first standardized framework unifying modeling concepts and notations for goals and intentions (mainly for non-functional requirements, quality attributes, and reasoning about alternatives) and scenarios (mainly for operational/functional requirements and reasoning about scenario interactions, performance, and high-level architecture). jUCMNav has been and continues to be instrumental in validating key concepts for the current standard as well as prototyping new concepts.

Interactions between dependent or conflicting aspects are a well-known problem with aspect-oriented development (and related paradigms). These interactions are potentially dangerous and can lead to unexpected or incorrect results when aspects are composed. To date, most aspect interaction detection methods have been based either on purely syntactic comparisons or have relied on heavyweight formal methods. We present a new approach that is based instead on lightweight semantic annotations of aspects. Each aspect is annotated with domain-specific markers and a separate influence model describes how semantic markers from different domains influence each other. Automated analysis can then be used both to highlight semantic aspect conflicts and to trade-off aspects. We apply this technique to early aspects, namely, aspect scenarios, because it is desirable to detect aspect interactions as early in the software lifecycle as possible. We evaluate the technique using an industrial case study and show that the technique detects interactions that cannot be discovered using syntactic techniques.