Made available in DSpace on 2014-06-12T15:53:11Z (GMT). No. of bitstreams: 2 arquivo1906_1.pdf: 1728491 bytes, checksum: 711dbaf0713ac324ffe904a6dace38d7 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Albertins de Lima, Lucas; Cezar Alves Sampaio, Augusto. Test case prioritization based on data reuse for Black-box environments. 2009. Dissertação (Mestrado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2009.

Motivation: Test case prioritization can prioritize test cases, optimize the test execution, save time and cost. There are many different methods for test case prioritization, test case prioritization method based on test case execution history is one kind of them. Based on the test case execution history, it’s easier to increase the rate of fault detection, hence we want to do a study about test case prioritization methods based on the test case execution history. Meanwhile, executing the feasible methods to compare the effectiveness of them. For the motivation of the thesis may be regarded as an example for experiencing approach for comparing test case prioritizations based on test case execution history, or as a study case for identifying the suitable methods to use and help improve the effectiveness of the testing process. Objectives: The aim of this thesis is to look for a suitable test case prioritization method that can support risk based testing, in which test case execution history is employed as the key criterion of evaluation. For this research, there are three main objectives. First, explore and summarize methods of test case prioritization based on test case history. Next, identify what are differences among the test case prioritization methods. Finally, execute the methods which we selected, and compare the effectiveness of methods. Methods: To achieve the first and the second study objectives, a systematic literature review has been conducted using Kitchenham guidelines. To achieve the third study objective, an experiment was conducted following Wohlin guidelines. Results: In our thesis: 1) We conducted a systematic literature review and selected 15 relevant literatures. We extracted data of the literatures and then we synthesized the data. We found that the methods have different kinds of inputs, test levels, maturity levels, validation and "automated testing or manual testing". 2) We selected two feasible methods from those 15 literatures, Method 1 is Adaptive test-case prioritization and Method 2 is Similarity-based test quality metric. We executed the methods within 17 test suites. Comparing the result of two methods and non-prioritization, the mean Average Percentage of Defects Found (APFD) of Adaptive test-case prioritization execution result (86.9%) is significantly higher than non-prioritization (51.5%) and Similarity-based test quality metric (47.5%), it means that the Adaptive test-case prioritization has higher effectiveness. Conclusion: In our thesis, existing test case prioritization methods based on test case execution history are extracted and listed out through systematic literature review. The summary of them and the description of differences can be available in the thesis. The 15 relevant literatures and the synthesized data may be as a guideline for relevant software researchers or testers. We did the statistical test for the experimental result, we can see two different test case prioritization methods have different effectiveness.

Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks.

Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks.

Context: Continuous Integration (CI) is a software development practice where a developer integrates a code into the shared repository. And, then an automated system verifies the code and runs automated test cases to find integration error. For this research, Ericsson’s CI system is used. The tests that are performed in CI are regression tests. Based on the time scopes, the regression test suites are categorized into hourly and daily test suits. The hourly test is performed on all the commits made in a day, whereas the daily test is performed at night on the latest build that passed the hourly test. Here, the hourly and daily test suites are static, and the hourly test suite is a subset of the daily test suite. Since the daily test is performed at the end of the day, the results are obtained on the next day, which is delaying the feedback to the developers regarding the integration errors. To mitigate this problem, research is performed to find the possibility of creating a learning model and integrating into the CI system, which can then create a dynamic hourly test suite for faster feedback. Objectives: This research aims to find the suitable machine learning algorithm for CI system and investigate the feasibility of creating self-learning test machinery. This goal is achieved by examining the CI system and, finding out what type data is required for creating the learning model for prioritizing the test cases. Once the necessary data is obtained, then the selected algorithms are evaluated to find the suitable learning algorithm for creating self-learning test machinery. And then, the investigation is done whether the created learning model can be integrated into the CI workflow to create the self-learning test machinery. Methods: In this research, an experiment is conducted for evaluating the learning algorithms. For this experimentation, the data is provided by Ericsson AB, Gothenburg. The dataset consists of the daily test information and the test case results. The algorithms that are evaluated in this experiment are Naïve Bayes, Support vector machines, and Decision trees. This evaluation is done by performing leave-one-out cross-validation. And, the learning algorithm performance is calculated by using the prediction accuracy. After obtaining the accuracies, the algorithms are compared to find the suitable machine learning algorithm for CI system. Results: Based on the Experiment results it is found that support vector machines have outperformed Naïve Bayes and Decision tree algorithms in performance. But, due to the challenges present in the current CI system, the created learning model is not feasible to integrate into the CI. The primary challenge faced by the CI system is, mapping of test case failure to its respective commit is no possible (cannot find which commit made the test case to fail). This is because the daily test is performed on the latest build which is the combination of commits made in that day. Another challenge present is low data storage. Due to this low data storage, problems like the curse of dimensionality and class imbalance has occurred. Conclusions: By conducting this research, a suitable learning algorithm is identified for creating a self-learning machinery. And, also identified the challenges facing to integrate the model in CI. Based on the results obtained from the experiment, it is recognized that support vector machines have high prediction accuracy in test case result classification compared to Naïve Bayes and Decision trees.

In this dissertation we investigate the applicability of different adaptive techniques to improve the effectiveness and efficiency of the regression testing. Initially, we introduce the concept of regression testing. We then perform a literature review of current practices and state-of-the-art regression testing techniques. Finally, we advance the regression testing techniques by performing four empirical studies in which we use different types of information (e.g. user session, source code, code commit, etc.) to investigate the effectiveness of each software metric on fault detection capability for different software environments. In our first empirical study, we show the effectiveness of applying user session information for test case prioritization. In our next study, we apply learning from the previous study, and implement a collaborative filtering recommender system for test case prioritization, which uses user sessions and change history information as input parameter, and return the risk score associated with each component. Results of this study show that our recommender system improves the effectiveness of test prioritization; the performance of our approach was particularly noteworthy when we were under time constraints. We then investigate the merits of multi-objective testing over single objective techniques with a graph-based testing framework. Results of this study indicate that the use of the graph-based technique reduces the algorithm execution time considerably, while being just as effective as the greedy algorithms in terms of fault detection rate. Finally, we apply the knowledge from the previous studies and implement a query answering framework for regression test selection. This framework is built based on a graph database and uses fault history information and test diversity in attempt to select the most effective set of test cases in term of fault detection capability. Our empirical evaluation of this study with four open source programs shows that our approach can be effective and efficient by selecting a far smaller subset of tests compared to the existing techniques.

Regression testing is the process of testing software to make sure changes to the software will not change the functionality. With growing test suites theneed to prioritize arises. This thesis explores how to weigh factors such as the number of fails detected, days since latest test case execution, and coverage. The prioritization is done over multiple test systems, software branches, and over many test sessions where the software can change in-between. With data provided by an industrial partner, we evaluate different ways to prioritize. The developed mathematical model could not cope with the size of the problem, whereas a simulated annealing approach based on said model proved highly successful. We also found that prioritizing test cases related to recent codechanges was effective. / Regressionstestning är processen att testa mjukvara för att säkerställa att ändringar av mjukvaran inte kommer att ändra funktionaliteten. Med växande testsviter uppstår behovet av att prioritera. Det här examensarbetet undersöker hur man väger faktorer som antalet upptäckta underkända testfall, dagar sedan testfallen senast kördes och täckning. Prioriteringen görs över flera testsystem, mjukvarugrenar och över många testsessioner där mjukvaran kan ändras däremellan. Med data från en industriell partner utvärderar vi olika sätt att prioritera. Den utvecklade matematiska modellen kunde inte hantera problemets storlek, medan en simulerad kylningsmetod baserad på denna modell visade sig vara mycket framgångsrik. Vi fann också att prioritering enligt ändringar som gjorts i mjukvaran var effetivt