Evaluating the Effectiveness of Mindmapping in Generating Domain Ontologies using OntoREM: The MASCOT Case Study
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1 Evaluating the Effectiveness of Mindmapping in Generating Domain Ontologies using OntoREM: The MASCOT Case Study Kelly Antonini University of the West of England, UK Dr. Mario Kossmann Airbus, UK Dr. Mohammed Odeh University of the West of England, UK Caroline Lange German Aerospace Center (DLR), Germany ABSTRACT: OntoREM is an Ontology-driven Requirements Engineering Methodology supported by software tooling, developed jointly by UWE (University of the West of England) and Airbus. OntoREM has been evaluated using a number of pilot cases in various domains at Airbus and it has been applied to a space mission called MASCOT (Mobile Space Asteroid Scout) developed by DLR-Bremen (Deutsches Zentrum für Luft- und Raumfahrt) and CNES (Centre National d'etudes Spatiales). As part of the OntoREM approach, high-level needs, goals and requirements are specified. Subsequently, requirements are analysed, modelled and validated using OntoREM. Mindmapping is a method that can significantly enhance human centred activities like requirements elicitation, analysis and validation by visualising information, and as such it is used by the OntoREM methodology. In this paper, the effectiveness of mindmapping has been evaluated using a reverseengineering approach which demonstrated effectiveness towards generating more correct, consistent and complete requirements specifications yet shortening the requirements engineering process time. Keywords: Mindmapping, OntoREM, Domain Ontology, MASCOT, Requirements Engineering, Requirements Analysis. 1. INTRODUCTION OntoREM is a semi-automated methodology developed for creating requirements specifications for systems in less time and at reduced costs, while improving the quality of such specifications. Applying the methodology at Airbus has resulted in considerable cost and time savings to develop aircraft operability requirements with a considerable increase in requirements reusability. It has also enabled estimating the cost and time needed in order to develop requirements ahead of the project start with a more accurate advanced assessment of risks involved. Collectively, the research and development of OntoREM resulted in the development of knowledge-driven semiautomated tools that enable the OntoREM process workflows, in particular the elicitation of domain knowledge, and the analysis and validation of needs, goals and requirements with relevant stakeholders and domain experts [3, 4, 9]. OntoREM has been applied to a space mission project called MASCOT. This case study will be used as an example of generating a domain ontology using mindmapping within OntoREM. Mindmapping is very valuable when developing requirements because it offers the possibility to easily present domain knowledge visually, including intermediate outcomes of the OntoREM approach to stakeholders who do not have to be familiar with OntoREM. For this paper, evaluating effectiveness means assessing the impact that mindmapping has on the application of the OntoREM process to a given domain or system, i.e. its outcomes in terms of improving communication and collaboration in the Requirements Engineering (RE) process. For example, improving the communication with stakeholders and knowledge management, and consequently improving requirement quality and traceability. A framework will be used to evaluate the effectiveness of reverse-engineering domain ontology mindmaps using OntoREM. Reverse-engineering in the scope of this research consists of going backwards in the development life cycle process, starting from the final domain or system (products of the life cycle) up to the requirements, analysing each part individually to examine the system at hand and represent it at a higher level of abstraction without modifying or changing it to the highest possible extent [9]. University of Nizwa, Oman December 9-11, 2014 Page 247
2 The paper is structured as follows. Section 2 presents related work, Sections 3 and 4 introduce MASCOT and describe the case study. Section 5 sheds light on the reverse-engineering approach to evaluate the effectiveness of using mindmapping as part of the OntoREM approach. Finally, Section 6 concludes the paper. 2. RELATED WORK Before looking at the MASCOT space programme and how OntoREM was applied to MASCOT, we start by briefly defining ontology and associated aspects. Castaneda et al. (2010) define ontology as an explicit formal specification of how to represent the entities that exist in a given domain of interest and the relationships that hold among them [1]. Zayed et al. (2013) write that since developers and engineers showed an interest in sharing and reusing knowledge across systems, ontologies have improved knowledge transformation and enhanced systems interoperability [9]. According to Kossmann et al. (2010) ontologybased solutions add value to RE processes because having a domain ontology means that a validated body of knowledge within a given domain is established. Re-use of domain knowledge and requirements is made possible by defining the problem and solution spaces as ontologies. Finally, it provides information in machine readable form, allowing automatic data inferencing [4]. In the work of El-Ghalayini et al. (2007), aimed at the possibility of generating conceptual data models from a given ontology, it appears that it is possible to generate a domain conceptual model from a given ontology subject to some validation that needs to be performed by the domain specialist before evolving this model into a global conceptual model [2]. To some extent, their research has some association to this research from the conceptual modelling side as it is aimed at studying the extent to which mindmaps can be used to generate systems requirement specifications within an ontologically pre-defined conceptual modelling development framework. small landing package to be flown on the Japanese Hayabusa-II space mission. Hayabusa-II, an ambitious follow-up to the successful Hayabusa mission that was launched in 2003, will be launched in the end of 2014 and will similarly to its predecessor - bring back samples from the near- Earth asteroid 1999 JU3, which it will follow from 2018 to MASCOT is an ultra-lightweight, highly integrated landing system with four scientific instruments on board, and will be deployed from the mother-spacecraft Hayabusa-II once it has arrived at the asteroid. It will then autonomously approach and land on the chosen target site, where it will as a scientific complement to the main mission perform investigations of the asteroid surface, e.g. its mineralogy, surface composition and its magnetic signature. MASCOT will gather information from up to three different landing sites, which it will reach by means of an innovative internal hopping mechanism, until its battery will run out after approximately 16 hours. In spite of the challenging mission requirements and schedule constraints, the MASCOT mission and its respective system design promise a unique combination of high performance, robustness and autonomy packed into the size of a shoe box (0.3m x 0.3m x 0.2m). Figure 1 - Artists Impression of the MASCOT Lander on the Asteroid, without Exterior Walls 4. APPLYING ONTOREM TO MASCOT 3. THE MASCOT SPACE MISSION PROGRAMME In this paper, we present the application of the OntoREM methodology to the German/French small body landing mission called MASCOT (Mobile Asteroid Surface Scout) [5, 10]. MASCOT (see Figure 1) has been developed as a carry-on Earlier, OntoREM was validated using case studies of a variety of domains at Airbus including aircraft operability for the wing design, IT tool development and the industrialisation for a specific aircraft program. With MASCOT, OntoREM has been applied to a space mission for the first time, in order to assess the suitability of the methodology in a space mission context, in particular in terms of University of Nizwa, Oman December 9-11, 2014 Page 248
3 the development of requirements for space missions, using the example case of MASCOT. Requirements specifications were developed following the OntoREM approach, based on the existing set of MASCOT system requirements that were re-used. Requirements elicitation and validation took place by means of interviews with key MASCOT stakeholders. High-level needs, goals and requirements were created, analysed, modelled and validated using OntoREM. From the case study, it appears that OntoREM is suitable for the development of requirements for space mission programmes to develop quality improvements when generating requirements specifications. The application of OntoREM to MASCOT has been successful. About 100% additional requirements have been generated (see Figure 2), probably due to the systematic approach of eliciting the MASCOT domain s problem space from several viewpoints, and the subsequent development of requirements from high-level needs via goal hierarchies, while re-using the existing system requirements. Figure 2 OntoREM MASCOT Requirements Statistics Thereby, OntoREM helped to discover areas that the MASCOT team had not explicitly captured in their system requirements. Also, by means of tool supported analysis of the generated MASCOT domain ontology including system and project requirements, as well as the stakeholder feedback received, it may be concluded that resulting requirements were of high quality in terms of correctness, completeness and consistency. Stakeholders (a stakeholder is someone who has direct or indirect influence on the project or who benefits in some way from it) are confronted with information stemming from other stakeholders so that they can either accept, extend or disagree with what has already been captured. Sometimes, when a stakeholder being interviewed did not have expertise in the area covered by a questions, information happened to be incomplete, in which case additional stakeholders to interview were identified. 4.1 Requirements Elicitation and Analysis One of the characteristics of the OntoREM methodology is that it brings together knowledge such as concerns, needs and requirements that belong to various stakeholders within a given project or programme. The first phase of the application of OntoREM to MASCOT was the elicitation of the problem space of the domain, which took place via semi-structured interviews. A commercial off the shelf mindmapping tool was used to visualise all the information that was collected from these stakeholder interviews. The resulting mindmap was named the MASCOT Project Domain (see Figure 3) and was divided into the following branches or headings: OntoREM Role (listing all the relevant MASCOT stakeholders); Problem Space Objective (listing the key objectives of the MASCOT mission); Problem Space Actor (containing all components of the MASCOT subsystem); Problem Space Life Cycle Phase (illustrating the different phases of development and operational use); and General Concerns (listing any other concern regarding the mission). Using mindmapping enables stakeholders to visualise the work in a very user-friendly manner. In this context, it was chosen to colour-code the information in the mindmap according to which stakeholder gave a specific piece of information. This method enabled to clearly visualise whether any conflicting information appeared from the interviews. Although in this research no such inconsistencies or conflicts were identified. All the information collected during the stakeholder interviews was used to specify the high-level needs and subsequently goal hierarchies for each such need identified. Within the mindmap links were created between needs and goals to make sure the needs and goals hierarchy covered all relevant information collected during the interviews (see Figure 4). When generating requirements, the link method (shown in Figure 4) was also used to link re-used requirements to their respective need. This method helped to make sure that all the re-used requirements were covered by a specific need. The use of mindmapping in structuring requirements elicitation interviews contributed to higher completeness, consistently and correctness in shorter process cycles. This was enabled by the knowledge-driven OntoREM approach, which is driven by relevant available information or University of Nizwa, Oman December 9-11, 2014 Page 249
4 knowledge rather than merely by a set of predefined steps. It mainly depends on the available relevant knowledge of a given application domain when generating the domain s requirements. Unlike merely process-driven approaches, not much rework is needed later on, which significantly improves the RE process and avoids additional delays or costs [9]. 4.2 Requirements Specification and Validation For each identified root goal in the goal hierarchies DLR system requirements were re-used where relevant, if the existing system requirement did not sufficiently cover a root goal, or if there was no previous requirement available, new system or project requirements were formulated. Figure 5 shows an example of need, root goal and requirements (new and re-used) displayed in MindManager [6]. Subsequently the MASCOT Project Domain mindmap was transferred into three separate mindmaps, Process Ontology, Problem Space Domain Ontology and Solution Space Ontology. These were formatted so that they could be read by the OntoREM-MindMapper (OMM) tool, which is part of the OntoREM methodology suite of tools. Figure 3 MASCOT Project Domain (MindManager) [6] Figure 4 MindManager Links [6] University of Nizwa, Oman December 9-11, 2014 Page 250
5 Figure 5 Example of Need, Root Goal and Requirements Figure 6 Traceability Generated Mindmap (MindManager) [6] These mindmaps were then automatically imported by OMM, in just a few seconds, and transformed into a MASCOT domain ontology in OWL DL (Ontology Web Language Description Logic) format [7], which is a global, standardised, machine readable format. Once created, this ontology file could be opened and edited by the Protégé ontology editor [8]. From this newly created ontology file, a traceability mindmap was generated by the OMM tool. This mindmap contained all the needs, goals, root goals and requirements as well as their traceability or dependencies. Finally, the mindmapping tool was used to display this traceability mindmap (see Figure 6). The validation of the information gathered in the mindmap including the five high-level needs and their goal hierarchies was carried out by giving the stakeholders an opportunity to review intermediate process outcomes and provide their feedback. 5. THE EVALUATION FRAMEWORK The effectiveness of the reverse-engineering of mindmap domain ontologies has been assessed by means of an evaluation framework based on a set of criteria developed during the application of OntoREM to MASCOT. University of Nizwa, Oman December 9-11, 2014 Page 251
6 The approach used is the gold model approach, which aims to evaluate the extent to which requirement mindmaps cover the substantial elements of requirements for a particular problem domain [2]. The proposed approach was applied in two stages: (1) visual validation by relevant stakeholders and experts, who validate the generated requirements ontology; (2) automatic verification carried out by the OMM tool. 5.1 The Generalised Evaluation Framework We applied the following method to evaluate: (1) Carry out requirements validation by domain experts to inform the quality of the generated requirements ontology, (2) Use the OMM tool metrics and apply them to the MASCOT lander, (3) Evaluate the effectiveness of these to this case study, (4) Triangulate the evaluation using domain experts evaluations in step (1) and the metrics application in steps (2) and (3), and hence have performed both static and dynamic evaluation of the reverseengineering approach advocated in this research, and, (5) Reflect on the evaluation with lessons learned, shortcomings, work in progress, and future directions. When domain knowledge that is stored in an OWL DL file has to be visualised for relevant stakeholders and domain experts in the form of a mindmap, information is transferred by the OMM tool from the owl environment, i.e. from an uploaded.owl file to the mindmap metamodel template. This derivation of specific mindmaps has to be correct and complete so that the mindmap corresponds exactly to the content of the owl file that is to be visualised. In the opposite direction, once work has been completed with stakeholders and domain experts based on which mindmap has been updated, domain knowledge has to be reverse-engineered into the owl ontology of the domain. This happens by means of the OMM tool that reads the updated mindmap file and reintegrates all information into the domain ontology in.owl format. This reverseengineering of the domain ontology has to be correct and complete, as well as consistent. It is of utmost importance that data and information integrity is ensured during these transfers of data. Multiple bi-directional iterations must be possible without losing any content, creating inconsistencies or creating wrong content within the domain ontology. In the present case, data integrity was monitored using two complementary approaches: the OMM tool automatically verified that the same number of classes, instances and properties and their values were present in both formats; and the human operator would be enabled to visually inspect traceability mindmaps (see Figure 5: needs-goalsrequirements) or other mindmaps that visualise specific classes with their relationships. Missing pieces of information were found to be detectable when these mindmaps were inspected. Table 2 illustrates the automatic checks performed by the OMM tool. While the automatic and visual checks together decreased the likelihood of any loss of data integrity during reverse-engineering between the mindmap and the ontology environment, and increased the probability of discovering any irregularities. It is not guaranteed that the approach is 100% effective, but it may be considered sufficient to efficiently monitor whether data integrity has been compromised. 5.2 Applying the Framework to MASCOT The first step in this framework relates to comparing the set of original requirements to the ones generated using the OntoREM approach, cross-checked through the associated domain experts. In the specific case of MASCOT, the first criterion is a quantitative comparison between the number of requirements generated using the DLR traditional approach, and the ones generated using the OntoREM approach. It is also quantitative because it relies on the human validation by relevant stakeholders or domain experts. The number of reused MASCOT system requirements generated by DLR and the number of new MASCOT system and project requirements generated by OntoREM are compared (Table 1). Re-used system require ments New project require ments New system N1 N2 N3 N4 N5 Total University of Nizwa, Oman December 9-11, 2014 Page 252
7 require ments Table 1 Requirement Statistics In total the requirements developed using OntoREM were 130, 66 of which re-used and 64 new. Applying OntoREM as a reapplication led to finding that about 100% additional requirements where validated. Therefore, one significant outcome of OntoREM was the creation of additional requirements that had not been explicitly identified before. The second criterion is a completeness check, which is carried out automatically by the OMM tool, to compare the number of subclasses, instances, needs, goals, requirements, added/deleted instances, and added/inconsistent property values. Figure 7 shows the OMM completeness check for the MASCOT application. This summarises the results of the generation process, including the number of the derived needs, goals and requirements. The completeness check functionality is capable of listing any missing needs, goals, or requirements that were not derived during the export process. Table 3 is a summary of the various values obtained by the OMM tool for the application of OntoREM to MASCOT. The OMM Metamodel feature was not used in the MASCOT case study. The fourth column represents the combined import of the problem space and solution space mindmaps for testing purposes, thus these readings demonstrate that data integrity has been enforced. e 2 OMM Automated Verifications Tabl University of Nizwa, Oman December 9-11, 2014 Page 253
8 Figure 7 OMM Completeness Checker Applied to MASCOT Table 3 OMM Automatic Checks Applied to MASCOT 6. CONCLUSION The above described approach shows that reverseengineering mindmaps to generate domain ontologies can assist in identifying the substantial semantics of a particular problem or solution domain, i.e. classes, instances, and the relations among them [2]. Although the requirements engineer involved in the requirements process had no background in systems engineering or requirements management, familiarisation and the requirements engineering process initiation was very swift for OntoREM. Mindmapping in general has enabled the visualisation of the work done very easily and clearly to the participating stakeholders, although these were not familiar with the OntoREM process to start with. The application of OntoREM to MASCOT has confirmed the effectiveness of the proposed evaluation framework introduced in performing static verification of reverse-engineering mindmaps of domain needs, goals and requirements following the OntoREM methodology. In addition the dynamic validation by stakeholders and domain experts informed the qualitative evaluation of the generated requirements when not yet formally ontologised using OWL-DL specifications. The metrics used informed the correctness check on the reverse-engineering completion from mindmaps to requirements models and vice versa. The challenge remains in how to triangulate between such static and dynamic verification/validation, which may be considered as skill-based and project impacted. Furthermore, it has been observed that the OntoREM process is applicable to a large variety of systems, and it generates improvements in requirement quality in the space industry, not only in the aerospace industry as it was demonstrated in the first application of OntoREM. Another lesson learnt is that having a general ontology is believed to be an advantage in the space industry as well. Although normally space missions are not built in series (on the contrary of airplanes for example), many space mission features are common to others, and therefore having a general space mission ontology is anticipated to bring time and cost saving, as well as improved requirement quality. Currently, a generalised space mission ontology is being developed jointly by the research team at the Software Engineering Research Group of UWE Bristol, Airbus Filton, and DLR Bremen in order to create a reusable domain ontology for Space Missions. Also, this developed generalised space mission ontology is being validated using the MASCOT mission. Work is in progress to publish the work and associated findings in key systems engineering and semantic knowledge journals. As further work, it is envisaged that a comparative analysis study of the cost, and resources based on the large scale studies case in use will further demonstrate the impact of OntoREM in reducing process cycle cost and time during the systems engineering life cycle. REFERENCES [1] Castaneda, V., Ballejos, L., Caliusco, M. L., Galli, M. R., The Use of Ontologies in Requirements Engineering, Global Journal of Researches in Engineering, Volume 10, Issue 6, pp. 2-8, [2] El-Ghalayini, H., Odeh, M., McClatchey, R., Engineering Conceptual Data Models from Domain Ontologies: A Critical Evaluation, International Journal of Information Technology and Web Engineering, Volume 2, Issue 1, pp , University of Nizwa, Oman December 9-11, 2014 Page 254
9 [3] Kossmann, M., Odeh, M., Ontology-driven Requirements Engineering A Case Study of OntoREM in the Aerospace Context, IS 10, INCOSE, [4] Kossmann, M., Odeh, M., Watts, S., Gillies, A., Ontology-driven Requirements Engineering with Reference to the Aerospace Industry, Journal of E- Technology, Volume 1, Issue 1, pp , [5] Lange, C., Findlay, R., Grimm, C., Grundmann, J. T., Ho, T., Witte, L., Zoest, T., how to build a 10 kg autonomous Asteroid landing package with 3 kg of instruments in 6 years? Systems Engineering challenges of a high-density deep space system in the DLR MASCOT project, SECESA, [6] Mindjet. MindManager. Mindjet. Accessed July 30, [7] OWL DL. OWL DL Semantics. Ontologies and Semantic Web. Accessed July 30, [8] Protégé. A free, open-source ontology editor and framework for building intelligent systems. Stanford University. Accessed July 30, [9] Zayed, R., Kossmann, M., Odeh, M., Bridging the Gap Between Human Thinking and Machine Processing in Developing and Maintaining Domain Knowledge, IS 13, INCOSE, [10] Ziach, C., Ho, T., Grimm, C., Findlay, R., Lange, C., Witte, L., Wrasmann, M., Biele, J., Ulamec, S., Spohn, T., Deleuze, M., Bibring, J., Okada, T., Yano, H., Hendrikse, J., The final development stages of MASCOT, a small asteroid lander to accompany Hayabusa-II, IAC, University of Nizwa, Oman December 9-11, 2014 Page 255
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