Computational Statistics of Data Science for Secured Software Engineering

Computational Statistics of Data Science for Secured Software Engineering

Raghavendra Rao Althar, Debabrata Samanta
DOI: 10.4018/978-1-7998-7701-1.ch005
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Abstract

The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role.
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Introduction

With the explosion of data globally and particularly in software engineering, there is a heavy focus needed on the analyst who can leverage from the knowledge hidden in this data. Data in the real world begs for automatic and semi-automatic methods of data exploration. Efforts put in over decades in artificial intelligence have provided excellent base work to develop upon. With the increasingly growing power of computation, it has reached the point of complexity involved to select areas to explore and focus our energy upon. Analytics in software development projects is an active area of exploration, with the knowledge hidden across the data like emails, source code, testing related reports, to name a few. All software engineers' activities leave back the information that can provide useful insights into the processes. All these have also resulted in collaboration among the professionals extending from building on to existing tools, learning best practices, and other areas. Analytics with the software data can help in real-time analytics such as event monitoring and reporting. It would take a significant amount of time and effort without the analytical capability being utilized. Also, in the case of application log monitoring, analytics's powers will provide the necessary efficiency and effectiveness for the process and create value. People, process, and technology being the key focus areas of the software development processes, exploration of the data need to revolve around these aspects. Productivity and quality of the software hold the key from a consumer point of view, which will decide on the user experience. With a large amount of data being processed, there is a need for more robust analytical approaches that can leverage these data. Understanding the stake of all the personnel involved in software engineering will be critical as well. Software analytics revolve around the computational capability to handle large data sets, analysis capabilities like data mining, pattern recognition, and visualization capabilities to derive insight. The software development area has many challenges to tackle. Issues related to the quality of the product, time to market, deployment associated challenges, and traceability capabilities across software development lifecycle are some of them that can be highlighted. Some of the information security breaches that have haunted the software development companies have put this area into top focus.

There is a struggle to bring all the stakeholders up to understand the importance of security in development. Though there is a large amount of knowledge all over, making sense of that information and putting them to use has not been an easy task. Data science has been explored to see if there can be useful learning and improvement to automate some of these areas. Since there is a need to ensure the security-related requirements are understood right at the beginning of the software development life cycle, the requirements management phase must be focused upon. All the stakeholder's involvement in this area needs to be understood and executed well. It is seen that there is a need for creating framework that can integrate all sources of knowledge in the industry and within the companies to help build the best practices across the industry. Understanding the domain of information security will play a key role in devising these systems. It cannot continue to be an area for experts to own, but everyone is needed to understand the need for security in software engineering. Apart from building solutions for the specific scenarios, there is a need to generalize the solutions as much as possible, leading to the institutionalization of the solutions effectively.

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