Being able to predict software quality is essential, but also it pose significant challenges in software engineering. Historical software project datasets are often being utilized together with various machine learning algorithms for fault-proneness classification.Unfortunately, the missing values in datasets have negative impacts on the estimation accuracy and therefore, could lead to inconsistent results. As a method handling missing data, K nearest neighbor (KNN) imputation gradually gains acceptance in empirical studies by its exemplary performance and simplicity. To date, researchers still call for optimized parameter setting for KNN imputation to further improve its performance. In the work, we develop a novel incomplete-instance based KNN imputation technique, which utilizes a cross-validation scheme to optimize the parameters for each missing value. An experimental assessment is conducted on eight quality datasets under various missingness scenarios. The study also compared the proposed imputation approach with mean imputation and other three KNN imputation approaches. The results show that our proposed approach is superior to others in general. The relatively optimal fixed parameter settings for KNN imputation for software quality data is also 2 determined. It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation.

Various black-box methods for the generation of test cases have been proposed in the literature. Many of these methods, including the category-partition method and the classification-tree method, follow the approach of partition testing, in which the input domain is partitioned into subdomains according to important aspects of the specification, and test cases are then derived from the subdomains. Though comprehensive in terms of these important aspects, execution of all the test cases so generated may not be feasible under the constraint of tight testing resources. In such circumstances, there is a need to select a smaller subset of test cases from the original test suite for execution. In this paper, we propose the use of white-box information to guide the selection of test cases from the original test suite generated by a black-box testing method. Furthermore, we have developed some techniques and algorithms to facilitate the implementation of our approach, and demonstrated its viability and benefits by means of a case study. . Downloaded from www.worldscientific.com by TEXAS CHRISTIAN UNIVERSITY on 02/05/15. For personal use only. 114 Y. T. Yu et al.due to the huge size of the input domain. Thus, a more practical approach is to construct a test suite, that is, a subset of the input domain for testing [2,3].The construction of a test suite is a critical component of the testing process, since it affects the scope and comprehensiveness of the test. Moreover, it largely determines the amount of testing resources required. Ideally, a test suite has to satisfy many requirements, but in reality some of these requirements may be conflicting. For example, the test suite should be as comprehensive as possible so that it is effective in detecting faults in the software, and it should be as small as possible in order to control the cost of the testing. A very comprehensive test suite containing a large number of test cases can be too costly to be practical, while a small but inadequate test suite may fail to detect some of the faults that exist.Various testing strategies have been proposed for systematic construction of a test suite. These strategies have been broadly classified as white-box (or code-based ) testing or black-box (or specification-based ) testing. The former refers to testing based on the information derived from the source code of the program, whereas the latter makes use of information from the specification. Both white-box and blackbox testing have their own merits and limitations; they are generally considered complementary to each other [4][5][6]. In principle, a testing methodology based on either black-box or white-box information alone is necessarily incomplete.Generally, white-box testing requires the test cases to execute the various components of the program code. Typical examples are (a) branch and path testing [7], which are based on the program's control flow structure, (b) data-flow testing [8,9], which exploits the definition-use associations of the program variables, and (c) mutation tes...

Many students need assistance in debugging to achieve progress when they learn to write computer programs. Face-to-face interactions with individual students to give feedback on their programs, although definitely effective in facilitating their learning, are becoming difficult to achieve with ever-growing class sizes. This paper proposes a novel approach to providing practical automated debugging advice to support students' learning, based on the strong relationship observed between common wrong outputs and the corresponding common bugs in students' programs. To implement the approach, we designed a generic system architecture and process, and developed a tool called Virtual Debugging Advisor (ViDA) that was put into use in classes in a university. To evaluate the effectiveness of ViDA, a controlled experiment and a survey were conducted with first year engineering students in an introductory computer programming course. Results are encouraging, showing that (a) a higher proportion of students could correct their faulty code themselves with ViDA enabled, (b) an overwhelming majority of respondents found ViDA helpful for their learning of programming, and (c) most respondents would like to keep ViDA enabled when they practice writing programs.

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