Using machine learning for automated detection of ambiguity in building requirements

The rule interpretation step is yet to be fully automated in the compliance checking process, hindering the automation of compliance checking. Whilst existing research has developed numerous methods for automated interpretation of building requirements, none can identify ambiguous requirements. As part of interpreting ambiguous clauses automatically, this research proposed a supervised machine learning method to detect ambiguity automatically, where the best-performing model achieved recall, precision and accuracy scores of 99.0%, 71.1%, and 78.2%, respectively. This research contributes to the body of knowledge by developing a method for automated detection of ambiguity in building requirements to support automated compliance checking

Assessing the Performance of Automated Model Extraction Rules

Automated Model Extraction Rules take as input requirements (in natural language) to generate domain models. Despite the existing work on these rules, there is a lack of evaluations in industrial settings. To address this gap, we conduct an evaluation in an industrial context, reporting the extraction rules that are triggered to create a model from requirements and their frequency. We also asses the performance in terms of recall, precision and F-measure of the generated model compared to the models created by domain experts of our industrial partner. Results enable us to identify new research directions to push forward automated model extraction rules: the inclusion of new knowledge sources as input for the extraction rules, and the development of specific experiments to evaluate the understanding of the generated models

Discovering affect-laden requirements to achieve system acceptance

Novel envisioned systems face the risk of rejection by their target user community and the requirements engineer must be sensitive to the factors that will determine acceptance or rejection. Conventionally, technology acceptance is determined by perceived usefulness and ease-of-use, but in some domains other factors play an important role. In healthcare systems, particularly, ethical and emotional factors can be crucial. In this paper we describe an approach to requirements discovery that we developed for such systems. We describe how we have applied our approach to a novel system to passively monitor users for signs of cognitive decline consistent with the onset of dementia. A key challenge was eliciting users’ reactions to emotionally charged events never before experienced by them at first hand. Our goal was to understand the range of users’ emotional responses and their values and motivations, and from these formulate requirements that would maximise the likelihood of acceptance of the system. The problem was heightened by the fact that the key stakeholders were elderly people who represent a poorly studied user constituency. We discuss the elicitation and analysis methodologies used, and our experience with tool support. We conclude by reflecting on the affect issues for RE and for technology acceptance

Application of machine learning techniques to the flexible assessment and improvement of requirements quality

It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality. In this work, we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations, and quality standards, with a high degree of automation. The domain experts contribute with an initial set of requirements that they have classified according to their quality, and we extract their quality metrics. We then use machine learning techniques to emulate the implicit expert’s quality function. We provide also a procedure to suggest improvements in bad requirements. We compare the obtained rule-based classifiers with different machine learning algorithms, obtaining measurements of effectiveness around 85%. We show as well the appearance of the generated rules and how to interpret them. The method is tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automatedThis research has received funding from the CRYSTAL project–Critical System Engineering Acceleration (European Union’s Seventh Framework Program FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement no 332830); and from the AMASS project–Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262)

Pendeteksian Ketidaklengkapan Kebutuhan dengan Teknik Klasifikasi pada Dokumen Spesifikasi Kebutuhan Perangkat Lunak

Rekayasa kebutuhan menghasilkan dokumen Spesifikasi Kebutuhan Perangkat Lunak (SKPL) dan merupakan tahapan yang kritis pada pengembangan perangkat lunak. Kesalahan yang terjadi pada proses rekayasa kebutuhan akan mempengaruhi ketidakberhasilan produk tersebut. Dokumen SKPL sering kali ditulis dengan bahasa alamiah. Karakteristik dokumen SKPL yang baik adalah benar, tidak rancu, konsisten, dapat diperingkatkan, dapat diverifikasi, dapat dimodifikasi, dapat ditelusuri, dan lengkap. Pada penelitian ini difokuskan pada kelengkapan. Kualitas spesifikasi kebutuhan bisa dinilai berdasarkan pernyataan kebutuhan atau dokumen kebutuhan. Spesifikasi kebutuhan yang lengkap secara jelas mendefinisikan semua situasi yang dihadapi sistem dan dapat dipahami tanpa melibatkan atau terkait pada kebutuhan lain. Penelitian ini bertujuan untuk membangun model klasifikasi pendeteksian ketidaklengkapan kebutuhan pada dokumen spesifikasi kebutuhan perangkat lunak yang ditulis dengan bahasa alamiah. Penelitian ini membuat corpus kebutuhan yang berisi pernyataan kebutuhan lengkap dan pernyataan kebutuhan tidak lengkap. Corpus merupakan kesepakatan hasil pelabelan secara manual oleh tiga orang ahli. Dari Corpus akan dilakukan pembangkitan kata kunci, ekstraksi fitur, pembangkitan data buatan, perankingan fitur, dan pembangunan model klasifikasi. Nilai performansi Gwet’s AC1 digunakan untuk mengetahui apakah model kerangka kerja yang dibangun dapat diandalkan dan dapat mendeteksi adanya ketidaklengkapan pada dokumen spesifikasi kebutuhan perangkat lunak. Berdasarkan hasil ujicoba dengan menggunakan kombinasi metode adaboost dan C4.5 diperoleh rata-rata indeks kesepakatan pada level moderate. Indeks kesepakatan antara ahli dengan kerangka kerja rata-rata berada pada tingkat moderate, ini lebih tinggi bila dibandingkan dengan indeks kesepakatan antar ahli sendiri yang hanya rata-rata pada tingkat fair. =================================================================Software requirements produces Software Requirements Specification (SRS) document and this is a critical stage in Software Development. Errors that occur in the software requirements will affect the failure of the product. SRS often written in natural language. Characteristics of a good SRS is correct, unambigous, consistent, rank for importance, verifiable, modifiable, traceable dan complete. In this study focused on completeness. The quality requirements specification can be assessed based on the statement or requirements document. Requirement specification is complete that defines precisely all the situations confronting the system and can be understood without related another requirements. This research purpose to establish a classification model incompleteness detection requirements in software requirements specification document written in natural language. This study makes corpus that contain statements of requirement complete and incomplete. Corpus is agreement of manual labeling by three experts. There will be keyword generation, features extraction, data generation synthesis, feature rank dan building classifier model. Corpus will be used for training and testing the classifier. Gwet’s AC1 performance value will be used to determine whether the classifier reliable and detect the presence of incompleteness in SRS. Based on the result of experiment using combination of method adaboost and C4.5 obtained average of agreement index at moderate level. The index of agreement between an expert with framework is moderate levels, this is higher when compared to an index of agreement between expert alone that are only average at fair levels

Aplicación de algoritmos de inteligencia artificial a la optimización de la calidad de requisitos mediante sugerencias automáticas de mejora

Mención Internacional en el título de doctorIncluye: Fe de erratas, pp. 107-110.La especificación de requisitos es de vital importancia en la planificación de un proyecto, es aquí en donde se especifican los límites y las bases sobre las que se va a sustentar dicho proyecto. Esta tesis se ha centrado en la ingeniería de requisitos. Siendo la creación de requisitos de calidad, la mejora de la eficiencia y la automatización de tareas los objetivos principales. Para poder alcanzar nuestros objetivos, se cuenta con 1035 requisitos que han sido previamente clasificados dependiendo de su calidad y posteriormente descritos por 26 atributos. De esta manera, los datos obtenidos sirven como muestra para extrapolar los conocimientos hacia cualquier otra base de datos. Una de las fortalezas de esta tesis es el alcance del proyecto, se ha diseñado un sistema capaz de adaptarse a cualquier base de requisitos. Independientemente de los clasificadores utilizados. Una barrera que se ha conseguido sobrepasar gracias a la utilización de los algoritmos genéticos. Para ello, se ha creado un método que se resume en los siguientes pasos: Primero se clasificarán los requisitos mediante la extracción de métricas de calidad que serán tomadas como base por el clasificador binario. En segundo lugar, se tomarán todos aquellos requisitos clasificados como de mala calidad y se utilizarán algoritmos genéticos para proponer soluciones de cambio optimizados de acuerdo con los costes de esfuerzo estimados. La principal conclusión que se puede extraer es que los algoritmos genéticos nos pueden ofrecer soluciones interesantes aplicables en ingeniería de requisitos. Obteniendo así un ahorro de costes, automatizando tareas y favoreciendo una planificación más sólida y eficiente en cualquier proyecto.The specification of requirements is of vital importance in the planning of a project, it is here where the limits and the bases on which the project will be based are specified. This research project has focused on requirements engineering. The main objectives are the creation of quality requirements, the improvement of efficiency, and the automation of tasks. To achieve our objectives, there are 1,035 requirements that have been previously classified depending on their quality and subsequently described by 26 attributes. In this way, the data obtained serve as a sample to extrapolate the knowledge to any other database. One of the strengths of the thesis is the scope of the project, a system capable of adapting to any base of requirements has been designed. Regardless of the classifiers used. A barrier that has been overcome thanks to the use of genetic algorithms. To do this, a method has been created that is summarized in the following steps: First, the requirements will be classified by extracting quality metrics that will be taken as a basis by the binary classifier. Second, all those requirements classified as bad will be taken and genetic algorithms will be used to propose optimized change solutions according to the estimated effort costs. The main conclusion that can be drawn is that genetic algorithms offer us interesting solutions applicable to requirements engineering. Thus obtaining cost savings, automating tasks, and favoring more solid and efficient planning in any project. project.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Álvarez Rodríguez.- Secretaria: Susana Irene Díaz Rodríguez.- Vocal: Cristina Paniagua Mur

Prototipado automático de sistemas de información transaccionales usando una especificación en lenguaje natural restringido

Las actividades del ciclo de vida del desarrollo de software (o SDLC por sus siglas en ingl´es) incluyen: an´alisis de requisitos, dise˜no de modelos, desarrollo, pruebas y mantenimiento. Las tareas tempranas de este ciclo (an´alisis de requisitos y dise˜no) tienen un amplio impacto en el ´exito del proyecto y por esto es fundamental que se lleven a cabo de una forma correcta. Estas actividades por supuesto est´an incluidas en el proceso de desarrollo de sistemas de informaci´on de procesamiento de transacciones. Estos sistemas de informaci´on son una de las maneras de generar valor desde la informaci´on producida en una organizaci´on. Adem´as, dan pie para generar sistemas de informaci´on de mayor complejidad y tambi´en permiten mejorar los procesos de toma de decisiones en las organizaciones. Sin embargo, los errores en las etapas tempranas del desarrollo de software son bastante comunes. Estos errores pueden llevar a dificultades a nivel de presupuesto y calendario en los proyectos de desarrollo de software, inclusive, a fracasos totales. Por esta raz´on, en esta tesis se propone dise˜nar, desarrollar y evaluar una metodolog´ıa para el prototipado autom´atico de sistemas de informaci´on transaccionales desde una especificaci´on en lenguaje natural restringido. Lo cual busca mejorar los procesos de an´alisis de requisitos y dise˜no de modelos puesto que permitir´ıa validar r´apidamente la funcionalidad del software, y as´ı, facilitar la detecci´on de errores y por ende su correcci´on temprana durante el desarrollo del proyecto. Para esto, en esta tesis se propone una metodolog´ıa de prototipado r´apido basada en un lenguaje natural restringido. Para crear este lenguaje natural restringido se usan como insumo dos lenguajes de especificaci´on populares: BPMN (Business Process Modeling Notation) y E-R (Entity - Relationship). Adem´as, para la generaci´on del prototipo funcional, se usar´an t´ecnicas de generaci´on de c´odigo fuente guidas por la sintaxis de este lenguaje. Como resultados de esta tesis, se llevaron a cabo dos implementaciones de la herramienta de generaci´on de c´odigo fuente. Adem´as, se presentan tres casos de estudio que permiten validar la aplicabilidad y efectividad de la metodolog´ıa propuesta: “Question cycle”, “Email Voting” y “Odoo clone”.Abstract. Software development life cycle (or SDLC) activities include: requirements analysis, models design, development, testing and maintenance. The early tasks of this cycle (requirements analysis and design) have a large impact on the success of the project and for this reason it is essential to perform them correctly. These activities are of course included in the process of developing transaction processing information systems. These information systems are one of the ways to generate value from the information produced in an organization. They also provide the basis for generating more complex information systems and also improve the decision-making processes in organizations. However, errors in the early stages of the software development process are quite common. These errors can lead to difficulties in the budget and schedule of software projects, or even, total failures. For this reason, the objective of this thesis is to design, develop and evaluate a methodology for the automatic prototyping of transactional information systems from a restricted natural language specification. This is aimed at improving the processes of analysis of requirements and design of models since it would allow to quickly validate the software functionality, and thus, facilitate the detection of errors and reduce costs by correct them early during the development. With that goal in mind, this thesis proposes a rapid prototyping methodology based on a restricted natural language. To create this restricted natural language, two popular specifi- cation languages are used as input resources: BPMN (Business Process Modeling Notation) and E-R (Entity - Relationship). In addition, source code generation techniques guided by the syntax of this language will be used for the generation of the functional prototype. As results of this thesis, two implementations of the source code generation tool were developed. In addition, three case studies were performed to validate the applicability and effectivity of the proposed methodology: “Question cycle”, “Email Voting”, and “Odoo clone”.Maestrí