Natural Language Processing for Detecting Forward Reference in a Document

Meyer’s seven sins have been recognized as types of mistakes that a requirements specialist are often fallen to when specifying requirements. Such mistakes play a significant role in plunging a project into failure. Many researchers were focusing in ambiguity and contradiction type of mistakes. Other types of mistakes have been given less attentions. Those mistakes often happened in reality and may equally costly as the first two mistakes. This paper introduces an approach to detect forward reference. It traverses through a requirements document, extracts, and processes each statement. During the statement extraction, any terms that may reside in the statement is also extracted. Based on certain rules which utilize POS patterns, the statement is classified as a term definition or not. For each term definition, a term is added to a list of defined terms. At the same time, every time a new term is found in a statement, it is check against the list of defined terms. If it is not found, then the requirements statement is classified as statement with forward reference. The experimentation on 30 requirements documents from various domains of software project shows that the approach has considerably almost perfect agreement with domain expert in detecting forward reference, given 0.83 kappa index value

Algorithms Comparison for Non-Requirements Classification using the Semantic Feature of Software Requirement Statements

Noise in a Software Requirements Specification (SRS) is an irrelevant requirements statement or a non-requirements statement. This can be confusing to the reader and can have negative repercussions in later stages of software development. This study proposes a classification model to detect the second type of noise, the non-requirements statement. The classification model that is built is based on the semantic features of the non-requirements statement. This research also compares the five best-supervised machine learning methods to date, which are support vector machine (SVM), naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and Decision Tree. This comparison aimed to determine which method can produce the best non-requirements classification, model. The comparison shows that the best model is produced by the SVM method with an average accuracy of 0.96. The most significant features in this non-requirement classification model are the requirements statement or non-requirements, id statement, normalized mean value, standard deviation value, similarity variant value, standard deviation normalization value, maximum normalized value, similarity variant normalization value, value Bad NN, mean value, number of sentences, bad VB score, and project id

PENDETEKSIAN ISTILAH BERBEDA PADA DOKUMEN SPESIFIKASI KEBUTUHAN PERANGKAT LUNAK (SKPL)

Abstrak. Dalam membuat dan menganalisa suatu dokumen SKPL diperlukan ketelitian dalam penyusunan SKPL. Dokumen SKPL harus jelas, lengkap, dan tidak ambigu. Istilah berbeda merupakan varian dari noise dalam suatu dokumen SKPL. Penelitian ini berfokus mengenai istilah berbeda yang dikenali sebagai sinonim pada pasangan kalimat dalam dokumen SKPL. Sinonim merupakan kata yang memiliki istilah yang berbeda dan bermakna sama. Perancangan metode terdiri dari proses pelatihan dan pengujian, yaitu prapemrosesan, menghitung kemiripan semantik dari pasangan kalimat dan menentukan nilai threshold. Sedangkan nilai Kappa untuk mengetahui perancangan metode dapat diandalkan dan digunakan untuk mendeteksi ketidakkonsisten istilah pada dokumen SKPL. Hasilnya adalah pasangan kalimat yang terdeteksi sebagai istilah berbeda.   Kata Kunci: dokumen SKPL, fakta, istilah berbeda, kemiripan semanti

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

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data

Metodología orientada a la optimización automática de la calidad de los requisitos

Las fases iniciales en los proyectos software marcan su desarrollo y resultado final. Defectos provocados en las fases iniciales afectan considerablemente a la calidad y alteran las fechas de finalización. Las organizaciones internacionales se han hecho eco de este problema y se dedican gran cantidad de esfuerzos en investigación para mejorar la calidad en las primeras etapas del desarrollo. Con esta iniciativa surge la ingeniería de requisitos, disciplina encargada de proporcionar procesos de ingeniería en el desarrollo de especificaciones de requisitos necesarias para definir proyectos con cierta complejidad. Por ello han surgido numerosas guías y estándares para asegurar la calidad de los requisitos que componen las especificaciones, evitando así que posibles defectos en los requisitos provoquen errores en el desarrollo y en el producto final. Una de las mayores dificultades relacionadas con la calidad en las especificaciones de requisitos es su dependencia a las exigencias de los distintos proyectos, y a las restricciones impuestas por los distintos dominios. En esta tesis se presenta una metodología que permite incluir las restricciones impuestas mediante el procesamiento de corpus de requisitos clasificados en función de su calidad por expertos del proyecto y del dominio. El objetivo de la metodología es proporcionar métodos automáticos para la optimización de la calidad en los requisitos de ingeniería. Para ello se propone un proceso para desarrollar un clasificador que permita emular la estimación de la calidad que otorgaría el experto del dominio a un requisito, un sistema de asesoramiento automático para mejorar la calidad de requisitos defectuosos y un método para la generación automática de patrones sintáctico-semánticos, que puedan ser empleados como guía en la redacción de nuevos requisitos asegurando así una composición estructuralmente correcta. Con el fin de corroborar las propuestas de la investigación, se presentan casos de estudio mediante el tratamiento de un corpus de requisitos proporcionado por el Grupo de Trabajo de la organización INCOSE (International Council on Systems Engineering 2016) y se analizan los resultados obtenidos.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Ambrosio Toval Álvarez.- Secretario: María Isabel Sánchez Segura.- Vocal: Susana Irene Díaz Rodrígue