Quality Assurance in Requirement Engineering

Requirement engineering is the most important process in software development life cycle. Quality assurance in requirement engineering has a great impact on the product quality. It checks whether the requirements meet the desired quality attributes i.e. adequacy, completeness, consistency etc. Quality Assurance of the requirement is important because the cost of requirements failure is very high. The proposed research is based on the survey of the quality assurance in requirement engineering. The major focus of this research paper is to analyze the quality parameters which assure the overcome of the issues related to the requirements. The research papers include brief overview of those parameters

Implementasi NLP Untuk Menilai Kualitas SKPL Berdasarkan Karakteristik Modifiable

Kebutuhan perangkat lunak adalah pernyataan suatu kebutuhan dan batasan serta kondisinya. Ilmu yang membahas pembentukan kebutuhan disebut dengan Requirement Engineering (RE). Salah satu produk dari RE adalah SKPL (Spesifikasi Kebutuhan Perangkat Lunak). Untuk memastikan bahwa kualitas perangkat lunak tinggi, maka kualitas SKPL pun harus tinggi. Perkembangan dalam SKPL selalu terjadi, maka SKPL harus selalu bisa dimodifikasi tanpa mengubah struktur kata dan dokumennya. Dalam standar IEEE hal ini disebut dengan sifat modifiable. Untuk mengukur kualitas modifiable dari SKPL, maka dalam penelitian ini telah dilakukan pengembangan tools berbasis Python. Setelah kualitas SKPL dihitung maka akan dilakukan pengujian program dengan perbaikan manual terhadap SKPL tersebut dan dibandingkan dengan hasil penghitungan awal. Hasil yang didapat adalah nilai kualitas Modifiable SKPL dalam penggunaan bahasa natural dan struktur dokumen yang dapat menjadi acuan perbaikan atau perlengkapan SKPL

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)