MEReq: A Tool to Capture and Validate Multi-Lingual Requirements

Within the era of globalisation that acknowledges differences and diversity, multiple languages have been increasingly used to capture requirements. This practice is particularly prevalent in Malaysia, where both Malay and English languages are used as a media of communication. Nevertheless, capturing requirements in multiple languages is often error-prone due to natural language imprecision being compounded by language differences. Considering that two languages may be used to describe requirements for the same system in different ways, we were motivated to develop MEReq, a tool which uses Essential Use Case (EUC) models to support capturing and checking the inconsistency occurring in English and Malay multi-lingual requirements. MEReq is tablet compatible to minimise time for on-site capture and validation of multi-lingual requirements. This paper describes the MEReq approach and demonstrates its use to capture and validate English and Malay requirements

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