@article{1098, abstract = {{An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.}}, author = {{Kim, Yeong-Su and Lee, Seung-Woo and Dollmann, Markus and Geierhos, Michaela}}, issn = {{2205-8494}}, journal = {{International Journal of Software Engineering for Smart Device}}, keywords = {{Natural Language Processing, Semantic Annotation, Machine Learning}}, number = {{2}}, pages = {{1--6}}, publisher = {{Global Vision School Publication}}, title = {{{Semantic Annotation of Software Requirements with Language Frame}}}, volume = {{4}}, year = {{2017}}, } @misc{119, author = {{Wever, Marcel Dominik}}, publisher = {{Universität Paderborn}}, title = {{{Active Learning of User Requirement Specifications in Dynamic Software Service Markets}}}, year = {{2017}}, } @inproceedings{120, abstract = {{Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}}, pages = {{1327----1334}}, title = {{{Active Coevolutionary Learning of Requirements Specifications from Examples}}}, doi = {{10.1145/3071178.3071258}}, year = {{2017}}, } @misc{100, author = {{Sergio Djoum Temdjim, Albin}}, publisher = {{Universität Paderborn}}, title = {{{Evaluation of Software Reputation Matching Based on App Reviews}}}, year = {{2017}}, } @misc{101, author = {{Rehmer, Lennart}}, publisher = {{Universität Paderborn}}, title = {{{Erweiterung eines kontextsensitiven Autovervollständigungstools zur natürlichsprachlichen Softwarespezifikation}}}, year = {{2017}}, } @phdthesis{195, author = {{Platenius, Marie Christin}}, publisher = {{Universität Paderborn}}, title = {{{Fuzzy Matching of Comprehensive Service Specifications}}}, year = {{2016}}, } @misc{197, author = {{Dollmann, Markus}}, publisher = {{Universität Paderborn}}, title = {{{Frag die Anwender: Extraktion und Klassifikation von funktionalen Softwareanforderungen aus User-Generated-Content}}}, year = {{2016}}, } @misc{173, author = {{Heck, Eduard}}, publisher = {{Universität Paderborn}}, title = {{{Ontologie-Erstellung mittels Text-Mining aus App-Marktplätzen am Beispiel des Google Marketplace}}}, year = {{2016}}, } @misc{174, author = {{Schwentker, Christoph}}, publisher = {{Universität Paderborn}}, title = {{{Ontologie-basierte Extraktion funktionaler Softwareanforderungen am Fallbeispiel mobiler Kommunikationsapplikationen}}}, year = {{2016}}, } @inproceedings{176, abstract = {{Users prefer natural language software requirements because of their usability and accessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present an automated approach for identifying and semantically annotating the on-topic parts of the given descriptions. It is designed to support requirement engineers in the requirement elicitation process on detecting and analyzing requirements in user-generated content. Since no lexical resources with domain-specific information about requirements are available, we created a corpus of requirements written in controlled language by instructed users and uncontrolled language by uninstructed users. We annotated these requirements regarding predicate-argument structures, conditions, priorities, motivations and semantic roles and used this information to train classifiers for information extraction purposes. The approach achieves an accuracy of 92% for the on- and off-topic classification task and an F1-measure of 72% for the semantic annotation.}}, author = {{Dollmann, Markus and Geierhos, Michaela}}, booktitle = {{Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)}}, location = {{Austin, TX, USA}}, pages = {{1807--1816}}, publisher = {{Association for Computational Linguistics (ACL)}}, title = {{{On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements}}}, year = {{2016}}, } @article{190, abstract = {{Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.}}, author = {{Platenius, Marie Christin and Shaker, Ammar and Becker, Matthias and Hüllermeier, Eyke and Schäfer, Wilhelm}}, journal = {{IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017}}, number = {{8}}, pages = {{739--759}}, publisher = {{IEEE}}, title = {{{Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic}}}, doi = {{10.1109/TSE.2016.2632115}}, year = {{2016}}, } @inproceedings{191, abstract = {{One purpose of requirement refinement is that higher-level requirements have to be translated to something usable by developers. Since customer requirements are often written in natural language by end users, they lack precision, completeness and consistency. Although user stories are often used in the requirement elicitation process in order to describe the possibilities how to interact with the software, there is always something unspoken. Here, we present techniques how to automatically refine vague software descriptions. Thus, we can bridge the gap by first revising natural language utterances from higher-level to more detailed customer requirements, before functionality matters. We therefore focus on the resolution of semantically incomplete user-generated sentences (i.e. non-instantiated arguments of predicates) and provide ontology-based gap-filling suggestions how to complete unverbalized information in the user’s demand.}}, author = {{Geierhos, Michaela and Bäumer, Frederik Simon}}, booktitle = {{Proceedings of the 21st International Conference on Applications of Natural Language to Information Systems (NLDB)}}, editor = {{Métais, Elisabeth and Meziane, Farid and Saraee, Mohamad and Sugumaran, Vijayan and Vadera, Sunil }}, isbn = {{978-3-319-41753-0}}, keywords = {{Requirement refinement, Concept expansion, Ontology-based instantiation of predicate-argument structure}}, location = {{Salford, UK}}, pages = {{37--47}}, publisher = {{Springer}}, title = {{{How to Complete Customer Requirements: Using Concept Expansion for Requirement Refinement}}}, doi = {{10.1007/978-3-319-41754-7_4}}, volume = {{9612}}, year = {{2016}}, } @misc{192, author = {{Reckhorn, Lena}}, publisher = {{Universität Paderborn}}, title = {{{Herausforderungen im Umgang mit unvollständigen Softwareanforderungen durch Semantic Role Labeling}}}, year = {{2016}}, } @misc{181, author = {{Stanco, Stefan}}, publisher = {{Universität Paderborn}}, title = {{{Modellierung von domänenspezifischen Ontologien im Anwendungsfall funktionaler Softwareanforderungen}}}, year = {{2016}}, } @techreport{221, author = {{Platenius, Marie Christin and Josifovska, Klementina and van Rooijen, Lorijn and Arifulina, Svetlana and Becker, Matthias and Engels, Gregor and Schäfer, Wilhelm}}, publisher = {{Universität Paderborn}}, title = {{{An Overview of Service Specification Language and Matching in On-The-Fly Computing (v0.3)}}}, year = {{2016}}, } @inproceedings{217, abstract = {{Today, cloud vendors host third party black-box services, whose developers usually provide only textual descriptions or purely syntactical interface specifications. Cloud vendors that give substantial support to other third party developers to integrate hosted services into new software solutions would have a unique selling feature over their competitors. However, to reliably determine if a service is reusable, comprehensive service specifications are needed. Characteristic for comprehensive in contrast to syntactical specifications are the formalization of ontological and behavioral semantics, homogeneity according to a global ontology, and a service grounding that links the abstract service description and its technical realization. Homogeneous, semantical specifications enable to reliably identify reusable services, whereas the service grounding is needed for the technical service integration. In general, comprehensive specifications are not availableand have to be derived. Existing automatized approaches are restricted to certain characteristics of comprehensiveness. In my PhD, I consider an automatized approach to derive fully-fledged comprehensive specifications for black-box services. Ontological semantics are derived from syntactical interface specifications. Behavioral semantics are mined from call logs that cloud vendors create to monitor the hosted services. The specifications are harmonized over a global ontology. The service grounding is established using traceability information. The approach enables third party developers to compose services into complex systems and creates new sales channels for cloud and service providers.}}, author = {{Schwichtenberg, Simon}}, booktitle = {{Proceedings of the 38th International Conference on Software Engineering Companion (ICSE)}}, pages = {{815--818}}, title = {{{Automatized Derivation of Comprehensive Specifications for Black-box Services}}}, doi = {{10.1145/2889160.2889271}}, year = {{2016}}, } @inproceedings{169, abstract = {{We apply methods of genetic programming to a general problem from software engineering, namely example-based generation of specifications. In particular, we focus on model transformation by example. The definition and implementation of model transformations is a task frequently carried out by domain experts, hence, a (semi-)automatic approach is desirable. This application is challenging because the underlying search space has rich semantics, is high-dimensional, and unstructured. Hence, a computationally brute-force approach would be unscalable and potentially infeasible. To address that problem, we develop a sophisticated approach of designing complex mutation operators. We define ‘patterns’ for constructing mutation operators and report a successful case study. Furthermore, the code of the evolved model transformation is required to have high maintainability and extensibility, that is, the code should be easily readable by domain experts. We report an evaluation of this approach in a software engineering case study.}}, author = {{Kühne, Thomas and Hamann, Heiko and Arifulina, Svetlana and Engels, Gregor}}, booktitle = {{Proceedings of the 19th European Conference on Genetic Programming (EuroGP 2016)}}, pages = {{278----293}}, title = {{{Patterns for Constructing Mutation Operators: Limiting the Search Space in a Software Engineering Application}}}, doi = {{10.1007/978-3-319-30668-1_18}}, year = {{2016}}, } @inproceedings{158, abstract = {{While requirements focus on how the user interacts with the system, user stories concentrate on the purpose of software features. But in practice, functional requirements are also described in user stories. For this reason, requirements clarification is needed, especially when they are written in natural language and do not stick to any templates (e.g., "as an X, I want Y so that Z ..."). However, there is a lot of implicit knowledge that is not expressed in words. As a result, natural language requirements descriptions may suffer from incompleteness. Existing approaches try to formalize natural language or focus only on entirely missing and not on deficient requirements. In this paper, we therefore present an approach to detect knowledge gaps in user-generated software requirements for interactive requirement clarification: We provide tailored suggestions to the users in order to get more precise descriptions. For this purpose, we identify not fully instantiated predicate argument structures in requirements written in natural language and use context information to realize what was meant by the user.}}, author = {{Bäumer, Frederik Simon and Geierhos, Michaela}}, booktitle = {{Proceedings of the 22nd International Conference on Information and Software Technologies (ICIST)}}, editor = {{Dregvaite, Giedre and Damasevicius, Robertas }}, isbn = {{978-3-319-46253-0}}, keywords = {{Natural language requirements clarification, Syntactically incomplete requirements, Compensatory user stories}}, location = {{Druskininkai, Lithuania}}, pages = {{549--558}}, publisher = {{Springer}}, title = {{{Running out of Words: How Similar User Stories Can Help to Elaborate Individual Natural Language Requirement Descriptions}}}, doi = {{10.1007/978-3-319-46254-7_44}}, volume = {{639}}, year = {{2016}}, } @phdthesis{150, author = {{Arifulina, Svetlana}}, publisher = {{Universität Paderborn}}, title = {{{Solving Heterogeneity for a Successful Service Market}}}, doi = {{10.17619/UNIPB/1-13}}, year = {{2016}}, } @inproceedings{160, abstract = {{A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only examples of event sequences that describe the behavior of the required software program. These examples are represented by simple sequence diagrams and are divided into two subsets: positive examples of required program behavior and negative examples of prohibited program behavior. The task is then to synthesize a generalized requirements specification that usefully describes the required software. We approach this problem by applying a genetic algorithm and evolve deterministic finite automata (DFAs). These DFAs take the sequence diagrams as input that should be either accepted (positive example) or rejected (negative example). The problem is neither to find the minimal nor the most general automaton. Instead, the user should be provided with several appropriate automata from which the user can select, or which help the user to refine the examples given initially. We present the context of our research ("On-The-Fly Computing"), present our approach, report results indicating its feasibility, and conclude with a discussion.}}, author = {{van Rooijen, Lorijn and Hamann, Heiko}}, booktitle = {{Proceedings of 24th IEEE International Requirements Engineering Conference (RE 2016)}}, pages = {{3----9}}, title = {{{Requirements Specification-by-Example Using a Multi-Objective Evolutionary Algorithm}}}, doi = {{10.1109/REW.2016.015}}, year = {{2016}}, }