Test case prioritization approaches in regression testing: A systematic literature review

Context Software quality can be assured by going through software testing process. However, software testing phase is an expensive process as it consumes a longer time. By scheduling test cases execution order through a prioritization approach, software testing efficiency can be improved especially during regression testing. Objective It is a notable step to be taken in constructing important software testing environment so that a system's commercial value can increase. The main idea of this review is to examine and classify the current test case prioritization approaches based on the articulated research questions. Method Set of search keywords with appropriate repositories were utilized to extract most important studies that fulfill all the criteria defined and classified under journal, conference paper, symposiums and workshops categories. 69 primary studies were nominated from the review strategy. Results There were 40 journal articles, 21 conference papers, three workshop articles, and five symposium articles collected from the primary studies. As for the result, it can be said that TCP approaches are still broadly open for improvements. Each approach in TCP has specified potential values, advantages, and limitation. Additionally, we found that variations in the starting point of TCP process among the approaches provide a different timeline and benefit to project manager to choose which approaches suite with the project schedule and available resources. Conclusion Test case prioritization has already been considerably discussed in the software testing domain. However, it is commonly learned that there are quite a number of existing prioritization techniques that can still be improved especially in data used and execution process for each approach

Test case prioritization technique based on string distance metrics

Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). There are different approaches and the process for each approach varies. Furthermore, these approaches are not well documented within the single TCP approach. Based on current studies, having an approach that has high coverage effectiveness (CE) and APFD rate, remains a challenge in TCP. The string-based approach is known to have a single string distance based metric to differentiate test cases that can improve the CE results. However, to differentiate precisely the test cases, the string distances require enhancement. Therefore, a TCP technique based on string distance metric was developed to improve CE and APFD rate. In this research, to differentiate precisely the test cases and counter the string distances problem, an enhanced string distances based metric with a string weight based metric was introduced. Then, the metric was executed under designed process for string-based approach for complete evaluation. Experimental results showed that the enhanced string metric had the highest APFD with 98.56% and highest CE with 69.82% in Siemen dataset, cstcas. Besides, the technique yielded the highest APFD with 76.38% in Robotic Wheelchair System (RWS) case study. As a conclusion, the enhanced TCP technique with weight based metric has prioritised the test case based on their occurrences which helped to differentiate precisely the test cases, and improved the overall scores of APFD and CE

A hybrid weight-based and string distances using particle swarm optimization for prioritizing test cases

Regression testing is concerned with testing the modified version of software. However, to re-test entire test cases require significant cost and time. To reduce the cost and time, higher average percentage fault detection (APFD) rate and faster execution to kill fault mutant are required. Therefore, to achieve these two requirements, an improvement to existing Test Case Prioritization (TCP) technique for a more effective regression testing is offered. A weight-hybrid string distance technique and prioritization using particle swarm optimization (PSO) is proposed. Distance between test cases and weight for each test case, and hybridization of both values for weight-hybrid string distance are calculated. This experiment was evaluated using Siemens dataset. Result obtained from this experiment shows that weight-hybrid string distance is capable of improving APFD values whereby APFD value for hybrid TFIDF-JC is equal to 97.37%, which shows the highest improvement by 4.74% as compared to non-hybrid JC. Meanwhile, for percentage of test cases needed to kill 100% fault mutants, hybrid TFIDF-M yields the lowest value, 22.88%, which shows a 76% improvement as compared to its non-hybrid string distance

A Measurement-Driven Process Model For Managing Inconsistent Software Requirements

Inconsistency is a pervasive issue in software engineering. Both general rules of inconsistency management and special case-based approaches to handling inconsistency have recently been considered. In this paper, we present a process model for handling requirements inconsistency within the Viewpoints framework. In this process model, when an inconsistency among viewpoints is detected, a set of candidate proposals for handling inconsistency will be generated using techniques fromMulti-agent automated negotiations. The proposals are then prioritized using an integrated measurement of inconsistencies. The viewpoints involved in the inconsistency will then enter the negotiations by being presented with the candidate proposals and thus selecting an acceptable proposal based on the priorities associated with each candidate proposal. To facilitate usability, in our process, we assume that the natural language requirements statements are first translated into corresponding logical formulas using a translator software. Moreover, the candidate proposals for handling inconsistency are also translated back from formal logic into natural language before being presented for selection

A Dynamic Multimedia User-Weight Classification Scheme for IEEE_802.11 WLANs

In this paper we expose a dynamic traffic-classification scheme to support multimedia applications such as voice and broadband video transmissions over IEEE 802.11 Wireless Local Area Networks (WLANs). Obviously, over a Wi-Fi link and to better serve these applications - which normally have strict bounded transmission delay or minimum link rate requirement - a service differentiation technique can be applied to the media traffic transmitted by the same mobile node using the well-known 802.11e Enhanced Distributed Channel Access (EDCA) protocol. However, the given EDCA mode does not offer user differentiation, which can be viewed as a deficiency in multi-access wireless networks. Accordingly, we propose a new inter-node priority access scheme for IEEE 802.11e networks which is compatible with the EDCA scheme. The proposed scheme joins a dynamic user-weight to each mobile station depending on its outgoing data, and therefore deploys inter-node priority for the channel access to complement the existing EDCA inter-frame priority. This provides efficient quality of service control across multiple users within the same coverage area of an access point. We provide performance evaluations to compare the proposed access model with the basic EDCA 802.11 MAC protocol mode to elucidate the quality improvement achieved for multimedia communication over 802.11 WLANs.Comment: 15 pages, 8 figures, 3 tables, International Journal of Computer Networks & Communications (IJCNC

Egoism, Altruism, and Market Illusions: The Limits of Law and Economics

The primary objective of this Article is to question assumptions in order to show that the conventional economic approach to law and public policy has limited value. The arguments are founded on empirical evidence drawn from many fields of study. An underlying theme is that the current application of economic analysis to law should be regarded as an interim step toward the integration of law with the behavioral, natural, and social sciences. Part I describes the two forms of the self-interest assumption more completely. This examination reveals that economics and the separate study of law and economics are caught in a dilemma, unable to embrace completely either of the two versions of the self-interest assumption. Egoism is an empty concept, and narrow self-interest asks us to ignore higher order preferences and altruism. Part II focuses on the narrow self-interest assumption and illustrate why its application to law is inappropriate. In Part III the problems of relying on choices, including market choices, as indicators of preference are examined

Weighted string distance approach based on modified clustering technique for optimizing test case prioritization

Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). String based approach had shown that applying a single string distance-based metric to differentiate the test cases can improve the APFD and coverage rate (CR) results. However, to precisely differentiate the test cases in regression testing, the string approach still requires an enhancement as it lacks priority criteria. Therefore, a study on how to effectively cluster and prioritize test cases through string-based approach is conducted. To counter the string distances problem, weighted string distances is introduced. A further enhancement was made by tuning the weighted string metric with K-Means clustering and prioritization using Firefly Algorithm (FA) technique for the TCP approach to become more flexible in manipulating available information. Then, the combination of the weighted string distances along with clustering and prioritization is executed under the designed process for a new weighted string distances-based approach for complete evaluation. The experimental results show that all the weighted string distances obtained better results compared to its single string metric with average APFD values 95.73% and CR values 61.80% in cstcas Siemen dataset. As for the proposed weighted string distances approach with clustering techniques for regression testing, the combination obtained better results and flexibility than the conventional string approach. In addition, the proposed approach also passed statistical assessment by obtaining p-value higher than 0.05 in Shapiro-Wilk’s normality test and p-value lower than 0.05 in Tukey Kramer Post Hoc tests. In conclusion, the proposed weighted string distances approach improves the overall score of APFD and CE and provides flexibility in the TCP approach for regression testing environment