物件導向軟體之架構(Object-Oriented Software Construction)探討

In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.

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From time series to complex networks: The visibility graph

Combinatorial Testing (CT) can detect failures triggered by interactions of parameters in the Software Under Test (SUT) with a covering array test suite generated by some sampling mechanisms. It has been an active field of research in the last twenty years. This article aims to review previous work on CT, highlights the evolution of CT, and identifies important issues, methods, and applications of CT, with the goal of supporting and directing future practice and research in this area. First, we present the basic concepts and notations of CT. Second, we classify the research on CT into the following categories: modeling for CT, test suite generation, constraints, failure diagnosis, prioritization, metric, evaluation, testing procedure and the application of CT. For each of the categories, we survey the motivation, key issues, solutions, and the current state of research. Then, we review the contribution from different research groups, and present the growing trend of CT research. Finally, we recommend directions for future CT research, including: (1) modeling for CT, (2) improving the existing test suite generation algorithm, (3) improving analysis of testing result, (4) exploring the application of CT to different levels of testing and additional types of systems, (5) conducting more empirical studies to fully understand limitations and strengths of CT, and (6) combining CT with other testing techniques.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Testing/Nie%20-%20Combinatorial%20Testing%20-%202012.pdf

A survey of combinatorial testing

Metaheuristic search techniques have been extensively used to automate the process of generating test cases, and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined “Search-based Software Testing” (SBST), has been used for a wide variety of test case generation purposes. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterizing how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well-designed and executed empirical studies.

/pdf/a-systematic-review-of-the-application-and-empirical-2zafnuqetq.pdf

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Fault%20Defect%20Prediction/Radjenovic%20-%20Software%20fault%20prediction%20metrics%20-%202013.pdf

Software fault prediction metrics

GA-based multiple paths test data generator

Algorithmic effort prediction models are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. In this paper, we present an adaptive fuzzy logic framework for software effort prediction. The training and adaptation algorithms implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available. Our validation experiment was carried out on artificial datasets as well as the COCOMO public database. We also present an experimental validation of the training procedure employed in the framework.

Adaptive fuzzy logic-based framework for software development effort prediction

Traditional approaches for software projects effort prediction such as the use of mathematical formulae derived from historical data, or the use of experts judgments are plagued with issues pertaining to effectiveness and robustness in their results. These issues are more pronounced when these effort prediction approaches are used during the early phases of the software development lifecycle, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Recent works have demonstrated promising results using approaches based on fuzzy logic. Effort prediction systems that use fuzzy logic can deal with imprecision; they, however, can not deal with uncertainty. This paper presents an effort prediction framework that is based on type-2 fuzzy logic to allow handling imprecision and uncertainty inherent in the information available for effort prediction. Evaluation experiments have shown the framework to be promising.

Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework

There has been an emerging trend to develop software using different components. In this way the cost of the software reduces and the developer is able to complete the system efficiently. The components' code may or may not be visible to the developer. Testing, in this case, requires the development of a set of test configurations that can be applied on the software. However, for software that comprises a large number of components, it is infeasible to test each and every test configuration within the limited testing budget and time. In this paper we propose a GA-based technique that identifies a set of test configurations that are expected to maximize pair-wise coverage, with the constraint that the number of test configurations is predefined. Although the paper primarily focuses on the interaction between software components, the idea can be applied to single code component testing. We performed some experiments using our proposed approach. The results were promising.

Pair-wise test coverage using genetic algorithms

Effective and efficient test data generation is one of the major challenging and time-consuming tasks within the software testing process. Researchers have proposed different methods to generate test data automatically, however, those methods suffer from different drawbacks. In this paper we present a genetic algorithm-based approach that tries to generate a test data that is expected to cover a given set of target paths. Our proposed fitness function is intended to achieve path coverage that incorporates path traversal techniques, neighborhood influence, weighting, and normalization. This integration improves the GA performance in terms of search space exploitation and exploration, and allows faster convergence. We performed some experiments using our proposed approach, where results were promising.

Moataz A. Ahmed

Papers

GA-based multiple paths test data generator

Adaptive fuzzy logic-based framework for software development effort prediction

Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework

Pair-wise test coverage using genetic algorithms

Genetic algorithm based test data generator