FASMM: Fast and Accessible Software Migration Method

International audienceWith the fast changes of development technologies, organizations often need to migrate their software from a source to a target technology that could comprise a shift in programming paradigm. This operation is not easy and requires precision and structuring. However, in small companies, due to lack of resources (workforce, time, budget...) the migration phase is frequently quickly done and not necessarily in an optimized way: functionalities are not implemented properly, the new architecture is loose and knowledge gained during the migration is not capitalized. This paper presents a method to guide developers in the migration of software functionalities based on model driven engineering techniques and allows capitalizing knowledge as transformation rules, to enable their reuse in future migration projects. This method was built from a case study in a French company that produces software training and support for critical applications

A Novel Approach for Process Mining : Intentional Process Models Discovery

International audienceSo 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

Method Association Approach: Situational construction and evaluation of an implementation method for software products

International audience— Software implementation is one of the important steps in a software engineering process. It consists of integrating software based services or components in business alignment with the organizational view and acceptance from the users' perspectives. However, this step is complex and not supported in detail by the existing design and implementation methods. When implementing a software product in a customer organization with a specific context, the problem of the choice of the method or its adaptation is crucial to ensure the implementation success. Software producing organizations have difficulty with the creation of the most suitable implementation method for their software products. Situational Method Engineering (SME) proposes solutions to create methods adapted to the project at hand. We propose an approach to build an implementation method based on the association of method fragments, offering two advantages: it facilitates (a) the modeling of fragments by using the Process Deliverable Diagram formalism (PDD) that has proved its efficacy and simplicity, and (b) the selection of fragments by using metrics to analyze them. We illustrate our proposal with a case study to create an implementation method for a personal health management software product. Keywords— Software product implementation method, situational method engineering, method association, feature, method fragment, project situatio

Supervised Intentional Process Models Discovery using Hidden Markov Models

Best Paper AwardInternational audienceSince 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' actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focuses on intentions rather than activities, in order to offer a better guidance through the processes, based on the reasoning behind the activities. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of intentions behind the recorded users' 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). This paper focuses on a supervised approach that allows discovering the intentions behind the users' activities traces and to compare them to the prescribed intentional process model

Découverte supervisée de Modèles de processus intentionnels basée sur les Modèles de Markov Cachés

National audienceCela fait plusieurs décennies que la communauté des Systèmes d'Information (SI) s'intéresse à la découverte 'automatisée' des modèles de processus. Certaines approches se basent sur les activités séquentielles (traces) effectuées par les acteurs du SI pour identifier les modèles de processus. Cependant, ces approches ne portent que sur les activités et les modèles identifiés sont donc orientés-activités. Les modèles de processus intentionnels se concentrent sur les intentions qui ont entraîné les activités plutôt que sur les activités elles-mêmes. Malheureusement, les approches de fouille de processus existantes ne tiennent pas compte de l'aspect caché des intentions derrière les activités. Nous pensons pouvoir découvrir les modèles de processus intentionnels à l'aide de techniques de fouille d'intention. Le but de cet article est de proposer l'utilisation de modèles probabilistes - les Modèles de Markov Cachés (MMC) - pour évaluer les intentions les plus probables à partir des traces. Cet article se concentre sur une approche supervisée pour découvrir les intentions sous-jacentes aux traces d'activités des utilisateurs et de les comparer au modèle de processus intentionnel initial

Automatic Process Model Discovery from Textual Methodologies: An Archaeology Case Study

International audience— 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

Intelligent Agile Method Framework

International audienceThe paper addresses the problem of the low quality of the process/product in the software development industry. In particular it deals with the issue of the low usage of software development methods, which is empirically proved to be one of the reasons for failures in software development projects and a contributor to the low quality of software. In this paper, we outline an approach that could help to improve the maturity of software development processes by circumventing the problems that hinder the use of disciplined approaches in the software development practice. The approach is based on the method engineering principles and represents a continuation of our past research in this field

Life-Threatening Laryngeal Edema and Hyponatremia during Hysteroscopy

We report on a 43-year-old patient undergoing a hysteroscopic myomectomy. After 80 minutes of operation, the patient developed laryngeal edema, requiring emergency tracheostomy. Hyponatremia (serum sodium 78 mmoL/L) indicated an irrigation fluid absorption. The patient developed shock, acute respiratory distress syndrome, acute renal failure, and diffuse intravascular coagulopathy. Resuscitation including continuous venovenous hemodiafiltration was required. Finally, the patient made a full clinical recovery. Hysteroscopy usually has low risks. However, absorption of the irrigation fluid can result in life-threatening fluid overload and electrolyte disturbances. Accurate fluid balancing and limiting the operation time may prevent these complications

Unsupervised discovery of intentional process models from event logs

International audienceResearch 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