Software Metric Literature Survey
Content
Research proposal on Metric Analysis for Autonomous Computing
Submitted by:
Ravi Shankar Singhal
School of Information and Communication Technology GAUTAM BUDDHA UNIVERSITY, GREATER NOIDA
Autonomic Computing As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems [3]. Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. However, while the vision of achieving self-management in computing systems is well established, the field still lacks a commonly accepted definition of what an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built.
Software Metrics Software metric is a measure of some property of a piece of software or its specifications. Since quantitative measurements are essential in all sciences, there is a continuous effort by computer science practitioners and theoreticians to bring similar approaches to software development [1]. The goal is obtaining objective, reproducible and quantifiable measurements, which may have numerous valuable applications in schedule and budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments.
Motivation From the literature survey I have arrived into the conclusion that the existing software metric tools interpret and implement the definitions of object-oriented software metrics differently. This delivers tool-dependent metrics results and has even implications on the results of analyses based on these metrics results. In short, the metrics based assessment of a software system and measures taken to improve its design differ considerably from tool to tool. To support our case, we conducted an experiment with a number of commercial and free metrics tools. We calculated metrics values using the same set of standard metrics for three software systems of different sizes. Measurements show that, for the same software system and metrics, the metrics values are tool depended. We also
defined a (simple) software quality model for "maintainability" based on the metrics selected. It defines a ranking of the classes that are most critical wrt. maintainability. Measurements show that even the ranking of classes in a software system is metrics tool dependent. The work has been done to propose an abstraction layer and an architecture supporting metrics collection and analysis. The proposed layer is a set of relations able to describe the structure of the source code. As a proof of concept, the paper describes the design and the implementation of Web Metrics, a tool implementing such technique. Web Metrics is part of PROM (PRO Metrics), an architecture designed to collect and analyze software metrics and Personal Software Process data.
The purpose of this work is to establish a standardized and quantitative definition of Autonomic Computing through the application of the Quality Metrics Framework described in IEEE Std 10611998. Through the application of this methodology, stakeholders were systematically analyzed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
Problem Statement
The Aim of proposal is to explore & propose some performance metric for autonomous computing in the following areas Study and in depth analysis of autonomic systems. Identification of factors affecting autonomic systems. Proposing Metrics for the factors like Complexity, Performance etc. which affect autonomic systems. Implementation and validation of purposed metrics.
References [1] Lincke Rüdiger, Lundberg Jonas, Löwe Welf “Comparing Software Metrics Tools” Software Technology Group School of Mathematics and Systems Engineering Växjö University, Sweden [2] Scotto Marco, Sillitti Alberto, Succi Giancarlo, Vernazza Tullio “Dealling with Software Mertrics Collection and Analysis: A Relational Approach”, STU2004. [3] Lin Paul, MacArthur Alexander, Leaney John “Defining Autonomic Computing: A Software Engineering Perspective” University of Technology, Sydney, Proceedings of the 2005 Australian Software Engineering Conference (ASWEC’05) 1530-0803/05 $20.00 © 2005 IEEE [4] Singh Pradeep Kumar, Sharma Arun, Kumar Amit, Saxena Ayush Autonomic Computing: A Revolutionary Paradigm for Implementing Self-managing Systems, International Conference on Recent Trends in Information Systems 2011. [5] Arun Sharma, Sandeep Chauhan, P S Grover, “Autonomic Computing: Paradigm shift for Software Development”, CSI Communications, September 2011, pp: 21-24. [6] R. Sterritt, B. Smyth and M. Bradley: PACT- Personal Autonomic Computing Tools. [7] Sam S. Lightstone, G. Lohman and D. Zilio: Toward Autonomic Computing with DB2 Universal Database.
Submitted by:
Ravi Shankar Singhal
School of Information and Communication Technology GAUTAM BUDDHA UNIVERSITY, GREATER NOIDA
Autonomic Computing As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems [3]. Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. However, while the vision of achieving self-management in computing systems is well established, the field still lacks a commonly accepted definition of what an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built.
Software Metrics Software metric is a measure of some property of a piece of software or its specifications. Since quantitative measurements are essential in all sciences, there is a continuous effort by computer science practitioners and theoreticians to bring similar approaches to software development [1]. The goal is obtaining objective, reproducible and quantifiable measurements, which may have numerous valuable applications in schedule and budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments.
Motivation From the literature survey I have arrived into the conclusion that the existing software metric tools interpret and implement the definitions of object-oriented software metrics differently. This delivers tool-dependent metrics results and has even implications on the results of analyses based on these metrics results. In short, the metrics based assessment of a software system and measures taken to improve its design differ considerably from tool to tool. To support our case, we conducted an experiment with a number of commercial and free metrics tools. We calculated metrics values using the same set of standard metrics for three software systems of different sizes. Measurements show that, for the same software system and metrics, the metrics values are tool depended. We also
defined a (simple) software quality model for "maintainability" based on the metrics selected. It defines a ranking of the classes that are most critical wrt. maintainability. Measurements show that even the ranking of classes in a software system is metrics tool dependent. The work has been done to propose an abstraction layer and an architecture supporting metrics collection and analysis. The proposed layer is a set of relations able to describe the structure of the source code. As a proof of concept, the paper describes the design and the implementation of Web Metrics, a tool implementing such technique. Web Metrics is part of PROM (PRO Metrics), an architecture designed to collect and analyze software metrics and Personal Software Process data.
The purpose of this work is to establish a standardized and quantitative definition of Autonomic Computing through the application of the Quality Metrics Framework described in IEEE Std 10611998. Through the application of this methodology, stakeholders were systematically analyzed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
Problem Statement
The Aim of proposal is to explore & propose some performance metric for autonomous computing in the following areas Study and in depth analysis of autonomic systems. Identification of factors affecting autonomic systems. Proposing Metrics for the factors like Complexity, Performance etc. which affect autonomic systems. Implementation and validation of purposed metrics.
References [1] Lincke Rüdiger, Lundberg Jonas, Löwe Welf “Comparing Software Metrics Tools” Software Technology Group School of Mathematics and Systems Engineering Växjö University, Sweden [2] Scotto Marco, Sillitti Alberto, Succi Giancarlo, Vernazza Tullio “Dealling with Software Mertrics Collection and Analysis: A Relational Approach”, STU2004. [3] Lin Paul, MacArthur Alexander, Leaney John “Defining Autonomic Computing: A Software Engineering Perspective” University of Technology, Sydney, Proceedings of the 2005 Australian Software Engineering Conference (ASWEC’05) 1530-0803/05 $20.00 © 2005 IEEE [4] Singh Pradeep Kumar, Sharma Arun, Kumar Amit, Saxena Ayush Autonomic Computing: A Revolutionary Paradigm for Implementing Self-managing Systems, International Conference on Recent Trends in Information Systems 2011. [5] Arun Sharma, Sandeep Chauhan, P S Grover, “Autonomic Computing: Paradigm shift for Software Development”, CSI Communications, September 2011, pp: 21-24. [6] R. Sterritt, B. Smyth and M. Bradley: PACT- Personal Autonomic Computing Tools. [7] Sam S. Lightstone, G. Lohman and D. Zilio: Toward Autonomic Computing with DB2 Universal Database.
