No abstract

Requirements prioritization is one of the key factors in deciding the success of the project and hence the software industry. One of the major concerns in software prioritization techniques is that the existing ranking techniques have a very modest support to different criteria used by stakeholders to present their ranking. The current techniques are not suitable for arriving at an optimized view of multiple stakeholders using multiple criteria. This research analyzes the issues in existing techniques. A web based decision support model using ELECTRE as the method for prioritization is proposed. ELECTRE is a multi-criteria decision making model that is proved to be effective in ranking several decision making problems. The proposed system takes input from multiple stakeholders using 100-point method. An optimized ranking is obtained using ELECTRE method. The developed system is validated using a pilot project and is found to be efficient in terms of saving cost of implementation and man-hours needed for implementation.

No abstract

Test case prioritization schedules the test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also detects the most severe faults at the earliest in its testing life cycle. Test case prioritization techniques have proved to be beneficial for improving regression testing activities. While code coverage based prioritization techniques are found to be studied by most scholars, test case prioritization based on requirements in a cost effective manner has not been used for studies so far. Hence, in this paper, we propose to put forth a model for system level Test Case Prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system test cases based on six factors: customer priority, changes in requirement, implementation complexity, usability, application flow and fault impact. The proposed prioritization technique is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.

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Test metrics succeed in analyzing the current level of maturity in testing and give a projection on the way to proceed with testing activities by allowing us to set goals and predict future trends. The objective of test metrics is to capture the planned and actual quantities: the effort, time and resources required to complete all the phases of development of the software project. Test case prioritization is an effective and practical technique in regression testing. It schedules test cases in order of precedence that increases their ability to meet some performance goals, such as code coverage, rate of fault detection. In this paper, we present a new metrics, based on varying requirement priorities, test case priorities, test case execution time, and fault severities. The case study illustrates that the rate of "units-of-test-case-priority-satisfied-per-unit-test-case-time" can be increased, and with improvement on testing quality and customer satisfaction. To assess the practicality of our approach, we apply it to a realistic example from the industrial projects. Also we summarize a test process measurement project of TECHZONE TM (Software development Concern with Testing Department) test teams, and analyze the effectiveness of set of metrics for cost, time, and quality to measure the quality of test process based on the results of the proposed metrics.

No abstract

Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified Rider optimization Algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state‐of‐the‐arts models by concerning the sanitization as well as restoration efficiency.