A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems

abstract: Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of classification of software requirements statements into categories easily comprehensible for developers for faster development and delivery, which till now was mostly done manually by software engineers - indeed a tedious job. However, most of the research was focused on classification of Non-functional requirements pertaining to intangible features such as security, reliability, quality and so on. It is indeed a challenging task to automatically classify functional requirements, those pertaining to how the system will function, especially those belonging to different and large enterprise systems. This requires exploitation of text mining capabilities. This thesis aims to investigate results of text classification applied on functional software requirements by creating a framework in R and making use of algorithms and techniques like k-nearest neighbors, support vector machine, and many others like boosting, bagging, maximum entropy, neural networks and random forests in an ensemble approach. The study was conducted by collecting and visualizing relevant enterprise data manually classified previously and subsequently used for training the model. Key components for training included frequency of terms in the documents and the level of cleanliness of data. The model was applied on test data and validated for analysis, by studying and comparing parameters like precision, recall and accuracy.Dissertation/ThesisMasters Thesis Engineering 201

A novel defect detection method for software requirements inspections

The requirements form the basis for all software products. Apparently, the requirements are imprecisely stated when scattered between development teams. Therefore, software applications released with some bugs, missing functionalities, or loosely implemented requirements. In literature, a limited number of related works have been developed as a tool for software requirements inspections. This paper presents a methodology to verify that the system design fulfilled all functional requirements. The proposed approach contains three phases: requirements collection, facts collection, and matching algorithm. The feedback results provided enable analysist and developer to make a decision about the initial application release while taking on consideration missing requirements or over-designed requirements

Automated Quality Assessment of Natural Language Requirements

High demands on quality and increasing complexity are major challenges in the development of industrial software in general. The development of automotive software in particular is subject to additional safety, security, and legal demands. In such software projects, the specification of requirements is the first concrete output of the development process and usually the basis for communication between manufacturers and development partners. The quality of this output is therefore decisive for the success of a software development project. In recent years, many efforts in academia and practice have been targeted towards securing and improving the quality of requirement specifications. Early improvement approaches concentrated on the assistance of developers in formulating their requirements. Other approaches focus on the use of formal methods; but despite several advantages, these are not widely applied in practice today. Most software requirements today are informal and still specified in natural language. Current and previous research mainly focuses on quality characteristics agreed upon by the software engineering community. They are described in the standard ISO/IEC/IEEE 29148:2011, which offers nine essential characteristics for requirements quality. Several approaches focus additionally on measurable indicators that can be derived from text. More recent publications target the automated analysis of requirements by assessing their quality characteristics and by utilizing methods from natural language processing and techniques from machine learning. This thesis focuses in particular on the reliability and accuracy in the assessment of requirements and addresses the relationships between textual indicators and quality characteristics as defined by global standards. In addition, an automated quality assessment of natural language requirements is implemented by using machine learning techniques. For this purpose, labeled data is captured through assessment sessions. In these sessions, experts from the automotive industry manually assess the quality characteristics of natural language requirements.% as defined in ISO 29148. The research is carried out in cooperation with an international engineering and consulting company and enables us to access requirements from automotive software development projects of safety and comfort functions. We demonstrate the applicability of our approach for real requirements and present promising results for an industry-wide application

Using Machine Learning and Graph Mining Approaches to Improve Software Requirements Quality: An Empirical Investigation

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection

Ernst Denert Award for Software Engineering 2020

This open access book provides an overview of the dissertations of the eleven nominees for the Ernst Denert Award for Software Engineering in 2020. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains eleven papers that describe the works by Jonathan Brachthäuser (EPFL Lausanne) entitled What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style, Mojdeh Golagha’s (Fortiss, Munich) thesis How to Effectively Reduce Failure Analysis Time?, Nikolay Harutyunyan’s (FAU Erlangen-Nürnberg) work on Open Source Software Governance, Dominic Henze’s (TU Munich) research about Dynamically Scalable Fog Architectures, Anne Hess’s (Fraunhofer IESE, Kaiserslautern) work on Crossing Disciplinary Borders to Improve Requirements Communication, Istvan Koren’s (RWTH Aachen U) thesis DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering, Yannic Noller’s (NU Singapore) work on Hybrid Differential Software Testing, Dominic Steinhofel’s (TU Darmstadt) thesis entitled Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules, Peter Wägemann’s (FAU Erlangen-Nürnberg) work Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems, Michael von Wenckstern’s (RWTH Aachen U) research on Improving the Model-Based Systems Engineering Process, and Franz Zieris’s (FU Berlin) thesis on Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work