Improving Software Systems By Flow Control Analysis

Using agile methods during the implementation of the system that meets mission critical requirements can be a real challenge. The change in the system built of dozens or even hundreds of specialized devices with embedded software requires the cooperation of a large group of engineers. This article presents a solution that supports parallel work of groups of system analysts and software developers. Deployment of formal rules to the requirements written in natural language enables using formal analysis of artifacts being a bridge between software and system requirements. Formalism and textual form of requirements allowed the automatic generation of message flow graph for the (sub) system, called the “big-picture-model”. Flow diagram analysis helped to avoid a large number of defects whose repair cost in extreme cases could undermine the legitimacy of agile methods in projects of this scale. Retrospectively, a reduction of technical debt was observed. Continuous analysis of the “big picture model” improves the control of the quality parameters of the software architecture. The article also tries to explain why the commercial platform based on UML modeling language may not be sufficient in projects of this complexity

How Do Real Options Concepts Fit in Agile Requirements Engineering?

Agile requirements engineering is driven by creating business value for the client and heavily involves the client in decision-making under uncertainty. Real option thinking seems to be suitable in supporting the client’s decision making process at inter-iteration time. This paper investigates the fit between real option thinking and agile requirements engineering. We first look into previously published experiences in the agile software engineering literature to identify (i) ‘experience clusters’ suggesting the ways in which real option concepts fit into the agile requirements process and (ii) ‘experience gaps’ and under-researched agile requirements decision-making topics which require further empirical studies. Furthermore, we conducted a cross-case study in eight agile development organizations and interviewed 11 practitioners about their decision-making process. The results suggest that options are almost always identified, reasoned about and acted upon. They are not expressed in quantitative terms, however, they are instead explicitly or implicitly taken\ud into account during the decision-making process at interiteration time

Understanding requirements dependency in requirements prioritization: a systematic literature review

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies

Case Study: Implementing Tools for Software Quality Assurance

Test tools have become ubiquitous in the practice of Software Quality Assurance. Every year, many millions of dollars are spent on specialized software to help manage requirements, test cases, or defects—or to automate test execution. But, after purchasing the software, many companies fail to successfully implement the tools. Given the cost of most test software, it’s not surprising that companies often make a second attempt at using the tools. This thesis describes a second-chance effort to use a suite of tools to manage various aspects of the test process. It examines some of the goals driving the adoption of test tools, as well as some of the challenges impeding it. It outlines a strategy, follows its execution, and describes the results. It looks at how tools are configured to align with the test process, and how the test process adapts to accommodate the tools. Finally, this study looks at how tool adoption is affected the opinions and attitudes of the test team, developers and management

Software Quality Assurance Using Agile Software Methodology in Education Assessment Industry

Contradictory directive between federal and state along with expensive investments in assessment strategies that may no longer work with new standards were just a few issues making educational assessment increasingly complicated. With evolution of rapid changing environment following student assessment practice, policy and analysis can be tough. This capstone project helped to build reliable and quality online solution which assisted to guard against system failures and scoring errors, and hold itself accountable for the security of the assessment process with use of cutting edge technology such as Agile in which incremental and iterative development of product through various cycles or feedback with involvement of cross functional and self-managing team helps to achieve product improvement

Software Quality Assurance Using Agile Software Methodology in Education Assessment Industry

Contradictory directive between federal and state along with expensive investments in assessment strategies that may no longer work with new standards were just a few issues making educational assessment increasingly complicated. With evolution of rapid changing environment following student assessment practice, policy and analysis can be tough. This capstone project helped to build reliable and quality online solution which assisted to guard against system failures and scoring errors, and hold itself accountable for the security of the assessment process with use of cutting edge technology such as Agile in which incremental and iterative development of product through various cycles or feedback with involvement of cross functional and self-managing team helps to achieve product improvement

Data-driven elicitation, assessment and documentation of quality requirements in agile software development

Quality Requirements (QRs) are difficult to manage in agile software development. Given the pressure to deploy fast, quality concerns are often sacrificed for the sake of richer functionality. Besides, artefacts as user stories are not particularly well-suited for representing QRs. In this exploratory paper, we envisage a data-driven method, called Q-Rapids, to QR elicitation, assessment and documentation in agile software development. Q-Rapids proposes: 1) The collection and analysis of design and runtime data in order to raise quality alerts; 2) The suggestion of candidate QRs to address these alerts; 3) A strategic analysis of the impact of such requirements by visualizing their effect on a set of indicators rendered in a dashboard; 4) The documentation of the requirements (if finally accepted) in the backlog. The approach is illustrated with scenarios evaluated through a questionnaire by experts from a telecom company.Peer ReviewedPostprint (author's final draft

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC