Researchers have employed a variety of techniques to extract underlying topics that relate to software development artifacts. Typically, these techniques use semi-unsupervised machine-learning algorithms to suggest candidate word-lists. However, word-lists are difficult to interpret in the absence of meaningful summary labels. Current topic modeling techniques assume manual labelling and do not use domainspecific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using Latent Dirichlet Allocation (LDA) from commit-log comments recovered from source control systems such as CVS and Bit-Keeper. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on two large-scale RDBMS projects: MySQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels relevant to these projects, which provides fresh insight into their evolving software development activities.

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Automated topic naming to support cross-project analysis of software maintenance activities

The issue-tracking systems used by software projects contain issues, bugs, or tickets written by a wide variety of bug reporters, with different levels of training and knowledge about the system under development. Typically, reporters lack the skills and/or time to search the issue-tracking system for similar issues already reported. As a result, many reports end up referring to the same issue, which effectively makes the bug-report triaging process time consuming and error prone. Many researchers have approached the bug-deduplication problem using off-the-shelf information-retrieval (IR) tools. In this work, we extend the state of the art by investigating how contextual information about software-quality attributes, software-architecture terms, and system-development topics can be exploited to improve bug deduplication. We demonstrate the effectiveness of our contextual bug-deduplication method at ranking duplicates on the bug repositories of the Android, Eclipse, Mozilla, and OpenOffice software systems. Based on this experience, we conclude that taking into account domain-specific context can improve IR methods for bug deduplication.

A contextual approach towards more accurate duplicate bug report detection and ranking

Context Recent developments in requirements engineering (RE) methods have seen a surge in using machine-learning (ML) algorithms to solve some difficult RE problems. One such problem is identification and classification of non-functional requirements (NFRs) in requirements documents. ML-based approaches to this problem have shown to produce promising results, better than those produced by traditional natural language processing (NLP) approaches. Yet, a systematic understanding of these ML approaches is still lacking. Method This article reports on a systematic review of 24 ML-based approaches for identifying and classifying NFRs. Directed by three research questions, this article aims to understand what ML algorithms are used in these approaches, how these algorithms work and how they are evaluated. Results (1) 16 different ML algorithms are found in these approaches; of which supervised learning algorithms are most popular. (2) All 24 approaches have followed a standard process in identifying and classifying NFRs. (3) Precision and recall are the most used matrices to measure the performance of these approaches. Finding The review finds that while ML-based approaches have the potential in the classification and identification of NFRs, they face some open challenges that will affect their performance and practical application. Impact The review calls for the close collaboration between RE and ML researchers, to address open challenges facing the development of real-world ML systems. Significance The use of ML in RE opens up exciting opportunities to develop novel expert and intelligent systems to support RE tasks and processes. This implies that RE is being transformed into an application of modern expert systems.

A Review of Machine Learning Algorithms for Identification and Classification of Non-Functional Requirements

Software repositories provide a deluge of software artifacts to analyze. Researchers have attempted to summarize, categorize, and relate these artifacts by using semi-unsupervised machine-learning algorithms, such as Latent Dirichlet Allocation (LDA). LDA is used for concept and topic analysis to suggest candidate word-lists or topics that describe and relate software artifacts. However, these word-lists and topics are difficult to interpret in the absence of meaningful summary labels. Current attempts to interpret topics assume manual labelling and do not use domain-specific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using LDA from commit-log comments recovered from source control systems. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on three large-scale Relational Database Management System (RDBMS) projects: MySQL, PostgreSQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels that are relevant to these projects, and provide fresh insight into their evolving software development activities.

Automated topic naming

Quality-attribute requirements describe constraints on the development and behavior of a software system, and their satisfaction is key for the success of a software project. Detecting and analyzing quality attributes in early development stages provides insights for system design, reduces risks, and ultimately improves the developers' understanding of the system. A common problem, however, is that quality-attribute information tends to be understated in requirements specifications and scattered across several documents. Thus, making the quality attributes first-class citizens becomes usually a time-consuming task for analysts. Recent developments have made it possible to mine concerns semi-automatically from textual documents. Leveraging on these ideas, we present a semi-automated approach to identify latent quality attributes that works in two stages. First, a mining tool extracts early aspects from use cases, and then these aspects are processed to derive candidate quality attributes. This derivation is based on an ontology of quality-attribute scenarios. We have built a prototype tool called QAMiner to implement our approach. The evaluation of this tool in two case studies from the literature has shown interesting results. As main contribution, we argue that our approach can help analysts to skim requirements documents and quickly produce a list of potential quality attributes for the system.

Uncovering quality-attribute concerns in use case specifications via early aspect mining

Recently, Quality Assurance concept has been developed increasingly to be included in many of our life existing fields; financial, industrial, trading, computing, etc. Software Quality Product Attributes (SWQAs) have been created as a matter of applying the QA concept on the results of web or desktop application development process, to fit the products with the organizational and global market standards and goals, and to provide it with a competitive advantage value. Web application or software product quality is composed of many attributes such as portability, usability, reliability, modularity. During the recent years, many researchers discussed and presented software attributes in their works which showed that till now there is a lack of consensus on the semantic of many of concepts and terminologies used in this field. Our work is focusing on studying Software Product Quality Attributes concepts and terminologies. We conduct several experiments to extract the main concepts for SWQAs. The results show that there is a number of concepts that are frequently used to describe these attributes. Summarizing and formalizing the semantic of the attributes into these concepts presents a common understanding and agreement on the semantic of SWQPAs which can be used by software engineers, researchers, practitioners, and stakeholders.

Towards an Ontology for Software Product Quality Attributes

The Riva approach is used to develop an object-based Business Process Architecture (BPA) that helps in capturing the full organizational strategies. However, due to the lack of effective tools that can generate Riva BPA models from available artifacts, producing such models is time consuming non-automatic process and it requires an exhaustive manual validation to ensure the development of error-free models. This paper introduces a novel and domain-independent tool titled ‘ARivaT’ to automatically generate Riva BPA models from available knowledge assets such as Units of Work diagrams. ARivaT is underpinned by a step-by-step methodological approach that automates the process of generating Units of Work, First-Cut, and Second-Cut Process Architecture Diagrams. It also provides stakeholders with insightful explanations to allow a precise understanding of business processes and workflow. Furthermore, ARivaT employs a rule-based mechanism to seamlessly validate all the generated Riva BPA models. A case study-based approach has been followed to evaluate the applicability of ARivaT to derive Riva BPA diagrams. The results of our experiment are promising since 82 percent time saving has been recorded when using ARivaT to generate Riva BPA diagrams in comparison to using general drawing tools such as MS Word and MS Visio. In addition, 69 percent time saving has been noted when performing the same task with ARivaT as opposed to specialized tools such as Camunda.

Ahmad A. Samhan

Papers

Towards an Ontology for Software Product Quality Attributes

ARivaT: A Tool for Automated Generation of Riva-Based Business Process Architecture Diagrams