Abstract Data protection compliance is critical from a requirements engineering (RE) perspective, both from a software development lifecycle (SDLC) perspective and regulatory compliance. Not including these requirements from the early phases of the SDLC can prove costly and challenging afterward. The general data protection regulation (GDPR) from the European Union (EU) sets a list of requirements that organizations working within its scope should satisfy. However, these requirements are complex to work with, as legal prose tends to be vague and imprecise, and not all requirements have received the same attention from researchers. This study aims to identify the research published in RE for helping compliance with regulatory data protection requirements. We gathered and analyzed 90 articles from 2016 to 2022 through a systematic mapping study. We analyzed key trends in the sample, such as year of publication, publication venue, type of research, interdisciplinarity in the author’s background, GDPR focus of compliance element, and type of proposal. Our main findings show ongoing interest, mostly published in conferences, in achieving overall compliance with the GDPR and consent as the most popular topics. Other topics, such as cookies or children’s data, did not receive significant attention. Research over the whole RE process has been done. 20 (22%) of the papers have authors affiliated with non-computer science; however, most research seems not interdisciplinary. We finally discuss gaps in the literature, possible future areas of research, and the importance of interdisciplinary research for regulatory data protection requirements in RE.

A software pattern is a reusable solution to address a commonly occurring problem within a given context when designing software. Using patterns is a common practice for software architects to ensure software quality. Many pattern collections have been proposed for a large number of application domains. However, because of the technology's recentness, there are only a few available collections with a lack of extensive testing in industrial blockchain applications. It is also difficult for software architects to adequately apply blockchain patterns in their applications, as it requires deep knowledge of blockchain technology. Through a systematic literature review, this paper has identified 120 unique blockchain-related patterns and proposes a pattern taxonomy composed of multiple categories, built from the extracted pattern collection. The purpose of this collection is to map, classify, and describe all the available patterns across the literature to help readers make adequate decisions regarding blockchain pattern selection. This study also shows potential applications of those patterns and identifies the relationships between blockchain patterns and other non-blockchain software patterns.

In the context of Product lines, the collaborative configuration process gets complicated when the configuration decisions of involved stakeholders are contradictory, which may lead to conflicting situations. Although considerable research has been devoted to collaborative configuration, little attention has been paid to conflict resolution. Moreover, most of existing approaches rely on a systematic process which constraint decisions of some stakeholders. In this paper, we propose a new collaborative configuration approach which allows conflict resolution based on stakeholders preferences expressed through a set of substitution rules. Based on such preferences, we delete the minimal set of conflicting configuration decisions which are identified using the Minimal Correction Subsets (MCSs) computing algorithm. An illustrating example and a tool prototype are presented to evaluate the applicability of our approach.

Blockchain has been praised for its capacity to hold data in a decentralized and tamper-proof way. It also supports the execution of code through blockchain's smart contracts, adding automation of actions to the network with high trustability. However, as smart contracts are visible by anybody on the network, the business data and logic may be at risk, thus companies could be reluctant to use such technology. This paper aims to propose a pattern that allows the execution of automatable legal contract clauses, where its execution states are stored in an on-chain smart-contract and the logic needed to enforce it wraps it off-chain. An engine completes this pattern by running a business process that corresponds to the legal contract. We then propose a pattern-based solution based on a real-life use case: transportation of refrigerated goods. We argue that this pattern guarantees companies pseudonymity and data confidentiality while ensuring that an audit trail can be reconstituted through the blockchain smart-contract to identify misbehavior or errors. This paper paves the way for a future possible implementation of the solution described, as well as its evaluation.

The demand for systems that continue on operation by adapting themselves in response to disturbing changes in their environment has increased in the last decades. Those systems, termed self-adaptive software (SAS) systems, should be developed with techniques and methods appropriated for analysing and designing this kind of systems, starting from the requirements phase. Several contributions propose approaches to improve the specification of requirements for those systems. This paper aims to review the most significant challenges still open in the domains of languages for requirements specification and methods for model verification of self-adaptive systems, independently of their particular application areas. More concretely, the main contribution of this paper is a list of ten challenges to achieve a better-defined specification of requirements for SAS systems, and a more effective verification of such specifications. These challenges are well worthy of being addressed in both communities, the requirements engineering (RE) and the SAS one.

In the software industry, product Lines has emerged as an attractive approach to perform planned reuse. Nevertheless, a product line solution is not appropriate for all the cases and requires some conditions to be implemented successfully. The literature offers several contributions regarding the adoption of product lines. However, only a few of these support decision-makers for taking well-informed decisions in favor or against following this approach. This paper presents APPLIES, a framework to (i) detect signals that could motivate an organization to adopt a product line approach (ii) assess to what extent a company is prepared for supporting the practices necessary to adopt a product line. APPLIES is grounded in a thematic analysis of the literature and was preliminarily evaluated with two experts through a survey and an interview. The results of the evaluation provide us insights to create an improved version of the proposal following a design-science method.

News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

Online news readers exhibit a very dynamic behavior. News publishers have been investigating ways to predict such changes in order to adjust their recommendation strategies and better engage the readers. Existing research focuses on analyzing the evolution of reading interests associated with news categories. Compared to these, we study also how relations among news interests change in time. Observations over a 10-month period on a German news publisher indicate that overall, the relations amid news categories change, but stable periods spanning months are also found. The reasons of these changes and how news publishers could integrate this knowledge in their solutions are subject to further investigation.

The strategic transition of media organizations to personalized information delivery has urged the need for richer methods to analyze the customers. Though useful in supporting the creation of recommender strategies, the current data mining techniques create complex models requiring often an understanding of techniques in order to interpret the results. This situation together with the recommender technologies deluge and the particularities of the news industry pose challenges to the news organization in making decisions about the most suitable strategy. Therefore, we propose process mining as a high-level, end-to-end solution to provide insights into the consumers' behavior and content dynamics. Specifically, we explore if it allows news organizations to analyze independently and effectively their data in order to support them in defining recommender strategies. The solution was implemented in a case study with the third largest news provider in Norway and yielded preliminary positive results. To our knowledge, this is the first attempt to apply a process mining methodology and adapt the techniques to support media industry with the recommender strategies.

E-learning is a promising research area, as they are expected to increase enrollment and improve the quality of education. Adaptive e-learning systems, traditionally focused on content personalization, are in need to cope with continuous changing requirements and changing environment. Indeed, the specification and the management quality attributes of such systems, supported throughout the whole lifecycle are still missing. In this paper, we propose continuous requirements monitoring that uses a constraint program to check the conformity of adaptive e-learning systems to their requirements and react properly when deviations occur at runtime. To this end, we specify system's requirements in the form of a dynamic software product line. A novel requirements engineering language that combines goal-driven requirements with features and claims is applied for the specification, from which the constraint program is automatically generated.

Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.

Product line engineering uses product line models to define the valid combinations of elements in a product and to configure them. Several modeling languages have been proposed to represent product line models. These languages have limits and they do not always fit the requirements of the context. For instance, in the industrial context, product lines contain often multi-instantiated features which are more difficult to model. This paper reports lessons learned during a project with an electronic supplier company called Rexel. Our objective in this study was to find out the appropriate modeling languages to model multi-instantiation, as required the Rexel's Electric Board that contains many multi-instantiated features. This paper presents (i) how three categories of modeling languages have been used in industry to model the multi-instantiation concept of an electric board; (ii) the limits and difficulties encountered with each category of modeling languages; and (iii) modeling strategies to handle multi-instantiation with each kind of language.

This paper presents the new release of VariaMos, a Java-based tool for defining variability modeling languages, modeling (dynamic) product lines and cyber-physical self-adaptive systems, and supporting automated verification, analysis, configuration and simulation of these models. In particular, we describe the characteristics of this new version regarding its first release: (1) the capability to create languages for modeling systems with variability, even with different views; (2) the capability to use the created language to model (dynamic) product lines; (3) the capability to analyze and configure these models according to the changing context and requirements; and (4) the capability to execute them over several simulation scenarios. Finally, we show how to use VariaMos with an example, and we compare it with other tools found in the literature.

Model simulation has demonstrated its usefulness in evaluation and decision-making for improving preliminary versions of artefacts before production. Particularly, one of the main goals of simulation is to verify model properties based on data collected from its execution. In this paper, we present the simulation capabilities of our REFAS framework for specifying requirements models for dynamic software products lines and self-adaptive systems. The simulation is controlled by a feedback loop and a reasoning engine that operates on the functional and non-functional requirements. The paper contribution is threefold. First, REFAS allows developers to evaluate and improve requirements models through their simulation capabilities. Second, REFAS provides rich feedback in its interactive simulations for the human modeller to make informed decisions to improve her model. Third, REFAS automates the generation of simulation scenarios required to verify the model adequacy and correctness. We evaluate our contribution by comparing the application of REFAS to a case study used in other approaches.

The research on requirements specification for self-adaptive systems has a growing interest in the academy and the industry. As a result, currently there exists different proposals for the specification of requirements for self-adaptive systems. Despite the momentum that this area has received in recent years, in the works reported in the literature we have identified shortcomings. We propose a new framework to represent the requirements of self-adaptive systems. This framework seeks to manage uncertainty and to be sufficiently expressive for self-adaptive systems, including the representation of all the relevant concepts. The concepts, represented in different views to be used in a 5-stage process. Specifically, we present: (i) a discussion of the challenges and problems encountered in the literature; (ii); our proposal to solve these challenges; and (iii) a case study of the problem and its application.

Research on guidance and method engineering has highlighted that many method engineering issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. However, software engineering process models are most often described in terms of sequences of activities. This paper presents a novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs. To do so, MMM uses Hidden Markov Models to model users' activities logs in terms of users' strategies. MMM also infers users' intentions and constructs fine-grained and coarse-grained intentional process models with respect to the Map metamodel syntax (i.e., metamodel that specifies intentions and strategies of process actors). These models are obtained by optimizing a new precision-fitness metric. The result is a software engineering method process specification aligned with state of the art of method engineering approaches. As a case study, the MMM is used to mine the intentional process associated to the Eclipse platform usage. Observations show that the obtained intentional process model offers a new understanding of software processes, and could readily be used for recommender systems.

So far, process mining techniques have suggested to model processes in terms of tasks that occur during the enactment of a process. However, research on method engineering and guidance has illustrated that many issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. This paper presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from process logs. MMM uses Hidden Markov Models to model the relationship between users' activities logs and the strategies to fulfill their intentions. The method also includes two specific algorithms developed to infer users' intentions and construct intentional process model (Map) respectively. MMM can construct Map process models with different levels of abstraction (fine-grained and coarse-grained process models) with respect to the Map metamodel formalism (i.e., metamodel that specifies intentions and strategies of process actors). This paper presents all steps toward the construction of Map process models topology. The entire method is applied on a large-scale case study (Eclipse UDC) to mine the associated intentional process. The likelihood of the obtained process model shows a satisfying efficiency for the proposed method.

Abstract: The paper addresses the problem of the low usage of software development methods in software development practice. This has been recognized as one of the key reasons for failures in software development projects and a contributor to the low quality of software. We introduce a novel approach that could help to improve the maturity of software development processes. The approach is based on the method engineering principles taking into account the limitations that hinder its use in practice. The main objective of our research is to show that the method engineering concepts are applicable in real settings and that could contribute to the higher quality of software development processes and their products. 1

Since several decades, discovering process models is a subject of interest in the Information System (IS) community. Approaches have been proposed to recover process models, based on the recorded sequential tasks (traces) done by IS's actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focus on the intentions underlying activities rather than activities, in order to offer a better guidance through the processes. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of the intentions behind the recorded user activities. We think that we can discover the intentional process models underlying user activities by using Intention mining techniques. The aim of this paper is to propose the use of probabilistic models to evaluate the most likely intentions behind traces of activities, namely Hidden Markov Models (HMMs). We focus on this paper on a supervised approach that allows discovering the intentions behind the user activities traces and to compare them to the prescribed intentional process model.

We present in this paper an experience in modeling a family of parking brake systems, with shared assets and alternative solutions, and relate them to the needs of Renault in terms of variability management. The models are realized using a set of customized tools for model based systems engineering and variability management, based on SysML models. The purpose is to present an industrial context that requires the adoption of a product line approach and of variability modeling techniques, outside of a pure-software domain. At Renault, the interest is in identifying variations and reuse opportunities early in the product development cycle, as well as in preparing vehicle configuration specifications during the systems engineering process. This would lead to lowering the engineering effort and to higher quality and confidence in carry-over and carry across based solutions. We advocate for a tight integration of variability management with the model based systems engineering approach, which needs to address methodological support, modeling techniques and efficient tools for interactive configuration, adapted for engineering activities.

Map formalism allows specifying processes with a high level of variability. However, this means many variation points, and therefore we need guidance to enact maps by customizing them. Traditional guidance consists in raising decision points to navigate in a map. The limit is that many decision points are raised at the same time, and the user (who enacts the map) does not know which decision to make first. Another kind of guidance, yet to be explored, consists in providing recommendations to the user. Such recommendations can be drawn from collections of profiles collected from map enactment traces using techniques from the data mining domain. This paper proposes a trace management system adapted to maps that was designed to support recommendation-based guidance. The paper shows how data mining algorithms can be used to find profile clusters in a collection of map enactment traces, used then to provide recommendations to the users.

Developing high quality systems depends on developing high quality models. An important facet of model quality is their consistency with respect to their meta-model. We call the verification of this quality the conformance checking process. We are interested in the conformance checking of Product Line Models (PLMs). The problem in the context of product lines is that product models are not created by instantiating a meta-model: they are derived from PLMs. Therefore it is usually at the level of PLMs that conformance checking is applied. On the semantic level, a PLM is defined as the collection of all the product models that can be derived from it. Therefore checking the conformance of the PLM is equivalent to checking the conformance of all the product models. However, we would like to avoid this naïve approach because it is not scalable due to the high number of models. In fact, it is even sometimes infeasible to calculate the number of product models of a PLM. Despite the importance of PLM conformance checking, very few research works have been published and tools do not adequately support it. In this paper, we present an approach that employs Constraint Logic Programming as a technology on which to build a PLM conformance checking solution. The paper demonstrates the approach with feature models, the de facto standard for modeling software product lines. Based on an extensive literature review and an empirical study, we identified a set of 9 conformance checking rules and implemented them on the GNU Prolog constraints solver. We evaluated our approach by applying our rules to 50 feature models of sizes up to 10000 features. The evaluation showed that our approach is effective and scalable to industry size models.

Product line engineering is a reuse-driven development paradigm based on the management of variability, which was successfully applied in information systems engineering and other domains. A common way to represent variability is with variability models that describe artefacts, and the dependencies between their various inflexions. Constraint programming, and in particular Boolean constraint programming, has been used so far to support analysis of variability models such as Feature-Oriented Domain Analysis (FODA) and the like. This paper goes a step further by using constraint programming to specify product lines. The focus on variability, variation points or dependencies is switched to the concept of constraints that apply to variables. The paper shows that this approach is richer than the one based on dependencies. For instance, many constraints that were needed in the cases we explored cannot be specified with dependencies of existing product line modelling languages. The approach was implemented in a prototype tool, and its scalability explored with industry case studies. These experiments show that constraint programming encompasses existing product line modelling languages such as FODA or OVM (Orthogonal Variability Model) and opens way to new possibilities such as reasoning simultaneously with different models during domain or application engineering.

Product Lines (PL) have proved an effective approach to reuse-based systems development. Several modeling languages were proposed so far to specify PL. Although they can be very different, these languages show two common features: they emphasize (a) variability, and (b) the specification of constraints to define acceptable configurations. It is now widely acknowledged that configuring a product can be considered as a constraint satisfaction problem. It is thus natural to consider constraint programming as a first choice candidate to specify constraints on PL. For instance, the different constraints that can be specified using the FODA language can easily be expressed using boolean constraints, which enables automated calculation and configuration using a SAT solver. But constraint programming proposes other domains than the boolean domain: for instance integers, real, or sets. The integer domain was, for instance, proposed by Benavides to specify constraints on feature attributes. This paper proposes to further explore the use of integer constraint programming to specify PL constraints. The approach was implemented in a prototype tool. Its use in a real case showed that constraint programming encompasses different PL modeling languages (such as FORE, OVM, or else), and allows specifying complex constraints that are difficult to specify with these languages.

A decision information system (DIS) is a specific component of information system specialized in the support of decision-making by means of online analytical processing tools. Data warehouses (DW), at the core of DIS, integrate and historize collections of data often designed starting from the operational components of the Information Systems (IS). Methods, techniques and tools widely used in the industry to engineer DIS focus, mostly, on designing DW models. However a few approaches were recently proposed to deal with the early phase of DIS development and maintenance, i.e. requirements engineering (RE). As for other specific kinds of systems, the expectation is that better value shall be delivered to DIS users (i.e. different sorts of decision maker such as executives, or managers, as well as control managers or operational) if more effort is spent on analyzing their requirements in early phase of DIS engineering projects. However, DIS is very specific kinds of systems. Therefore the question is how specific should RE approaches be when developing a DIS? To addresses this question, we undertook an in depth analysis of (a) the specificities of DIS systems that shall have an impact on RE approach (b) expectations from DIS project stakeholders, and (c) gap analysis of DIS specific RE approaches and traditional RE approaches. The analysis is structured around the three main axes of RE approaches, i.e. formalization, specification, and consistency. The paper shows that although on a fundamental level RE for DIS approaches does not show conceptual differences with traditional RE for IS approaches, some differences exist in the way of working.

Constraint Programming (CP), and in particular boolean CP, has been used so far to support analysis of variability models such as Feature Oriented Domain Analysis (FODA) and like. This paper goes a step further by using constraint programming to specify the Product Line (PL). The vision of variability and variation points completely disappear, and the focus is completely on constraints. Of course, the constraints can originate from various PL models, which can still be used to guide product analysis. However, owing to the unique constraint program, the analysis of the different viewpoints represented by each of these models is achieved in an integrated way.

Product line models (PLM) are important artifacts in product line engineering. Due to their size and complexity, it is difficult to detect defects in PLMs. The challenge is however important: any error in a PLM will inevitably impact configuration, generating issues such as incorrect product models, inconsistent architectures, poor reuse, difficulty to customize products, etc. Surveys on feature-based PLM verification approaches show that there are many verification criteria, that these criteria are defined in different ways, and that different ways of working are proposed to look for defect. The goal of this paper is to systematize PLM verification. Based on our literature review, we propose a list of 23 verification criteria that we think cover those available in the literature.

Product lines (PL) modeling have proven to be an effective approach to reuse in software development. Several variability approaches were developed to plan requirements reuse, but only little of them actually address the issue of deriving product requirements. This paper presents a method, RED-PL that intends to support requirements derivation. The originality of the proposed approach is that (i) it is user-oriented, (ii) it guides product requirements elicitation and derivation as a decision making activity, and (iii) it provides systematic and interactive guidance assisting analysts in taking decisions about requirements. The RED-PL methodological process was validated in an industrial setting by considering the requirement engineering phase of a product line of blood analyzers.

The art of ERP implementation stands in matching ERP features with the requirements of an organisation so as to define how to adapt the system and/or the organisation to reach a synergic functioning. A number of approaches have been developed in practice and elaborated in research to solve the issues posed by this matching activity, for example to solve the conceptual mismatch issue or to systematise similarity analysis between different kinds of models such as business process models and system functionality models. One aspect is not often considered in these approaches: it is the opportunity to reuse experience from one ERP implementation project to another. This paper presents a reuse-based requirements elicitation method for ERP integration. The method was developed in an action research approach in a project held in a charity association that wanted to integrate Microsoft's Dynamics NAV.

PLM approaches are becoming a prominent approach in the Software Engineering and Systems Engineering PL contexts. The idea behind PLM is to focus on artifacts that are shared and that vary from one product to the other, so as to facilitate reuse and adaptation. Gains are expected in terms of time to market, consistency across products, easier identification of requirements for future products, costs reduction, better flexibility, and better management of change requirements. While most of the recent research works are focusing on methods and modeling techniques, little has been done so far with respect to PLM tools and their ability to answer industry needs. A study was thus undertaken in collaboration with a group of industrials to evaluate existing PLM tools. The purpose of the study was twofold: to understand the salient characteristics of PLM tools, and to evaluate the ability of existing tools to satisfy the expectations of industrials. The study was conducted using (a) a state of the art of PLM methods, (b) an analysis grid developed by the industrial partners to analyze the characteristics ofPM tools in general, and (c) interviews with our industrial partners. This paper reports our analysis under the form of a benchmark aimed at being used by industrials to select existing tools, and discusses open issues in the domain of RE for PL.

Complex artefacts, such as information systems (IS), have multiple aspects and components: business processes, databases, architecture, or software. It is generally agreed that all these should be kept consistent over time. One major issue to preserve consistency is when required evolutions affect multiple aspects or components of the system at the same time. As each evolution requirement can have an impact onto several projects, teams, engineering domains, viewpoints, or system components, the question of "is the consistency link preserved by this requirement?" has to be continuously raised. This paper presents: (i) a framework that defines challenges for RE caused by coevolution; and (ii) an approach to solve some of these RE-related coevolution challenges. The framework was developed based on our experience in three IS evolution projects: ERP installation, baselining of an IS across subsidiaries, and business process improvement driven IS evolution. Each challenge identified in the framework is discussed with respect to our experience with practice and state of the art methods. Our approach was developed for the business process improvement driven IS evolution project, then generalised for the IS baselining project. The approach is presented, and then illustrated with the case of the latter project.

This paper presents a repository-based tool for sharing RE (empirical or theoretical) methodological knowledge, and improving it collaboratively. Methods are documented in our tool with methodological patterns. The originality of our tool is threefold: (i) the structure of methodological patterns can evolve, (ii) it is also possible to adapt the structure of the pattern repository, and (iii) configuration management features are proposed to enrich the content of the repository collaboratively.

Eliciting requirements is an important issue of system development projects. Some approaches propose to identify requirements by analysing system malfunctioning. Different sources of malfunctioning are dealt with by these approaches: obstacles, conflicts, risks, etc. Our proposal is to analyse each of these sources of malfunctioning using a single notion that we call "threat". We propose to use this notion in a method that guides the identification and analysing of each of these sources of malfunctioning. The method helps eliciting requirements to prevent the threat. A threat is defined by a number of variables. This paper presents a literature review of all threats that relate to users. The review is based on a framework that includes several perspectives to analyse user error. The user threats is part of a global threats classification that also covers hardware, environment, design and project types of threats.

Information system adaptation and business process change pose specific issues. Based on our experience in the industrial context, we propose a methodological framework to tackle these issues. Among the lessons learned from the experimentation of this framework, the need to formally connect the different models used was identified. The approach presented in this paper advocates the use of a link meta-model to define this connection. Combined with business goal modelling and analysis of the gaps between models of the current and future situations, the link meta-model helps guiding the analysis of business process change for information system adaptation. The link meta model is applied with the case study of an evolving Pizzeria company.

This paper presents results from the first of two empirical studies which examine the effectiveness of guidelines for use case authoring. The ESPRIT 21.903 CREWS long-term research project has developed style and content guidelines for authoring use cases for requirements acquisition and validation. The effectiveness of these guidelines has been evaluated under different conditions. Results indicate that: the authoring guidelines improve the overall quality of the use case prose; the different guidelines work differently and with different levels of efficiency; and use cases are never entirely correctly written; thus, they can be systematically corrected. The paper details a qualitative and quantitative comparison between guided and non-guided use case authoring. It outlines lessons learned and implications for the CREWS software tools design.

Even though goal modeling is an effective approach to requirements engineering, it is known to present a number of difficulties in practice. The paper discusses these difficulties and proposes to couple goal modeling and scenario authoring to overcome them. Whereas existing techniques use scenarios to concretize goals, we use them to discover goals. Our proposal is to define enactable rules which form the basis of a software environment called L'Ecritoire to guide the requirements elicitation process through interleaved goal modeling and scenario authoring. The focus of the paper is on the discovery of goals from scenarios. The discovery process is centered around the notion of a requirement chunk (RC) which is a pair. The paper presents the notion of RC, the rules to support the discovery of RCs and illustrates the application of the approach within L'Ecritoire using the ATM example. It also evaluates the potential practical benefits expected from the use of the approach.