Making investment decision on various available stocks in the market is a challenging task. Econometric and statistical models, as well as machine learning and data mining techniques, have proposed heuristic based solutions with limited long-range success. In practice, the capabilities and intelligence of financial experts is required to build a managed portfolio of stocks. However, for non-professional investors, it is too complicated to make subjective judgments on available stocks and thus they might be interested to follow an expert's investment decision. For this purpose, it is critical to find an expert with similar investment preferences. In this work, we propose to benefit from the power of Social Network Analysis in this domain. We first build a social network of financial experts based on their publicly available portfolios. This social network is then used for further analysis to recommend an appropriate managed portfolio to non-professional investors based on their behavioral similarities to the expert investors. This approach is evaluated through a case study on real portfolios. The result shows that the proposed portfolio recommendation approach works well in terms of Sharpe ratio as the portfolio performance metric.

The user-centric query interface is very common application that allows expressing both the input and the output using fuzzy terms. This is becoming a need in the evolving internet-based era where web-based applications are very common and the number of users accessing structured databases is increasing rapidly. Restricting the user group to only experts in query coding must be avoided. The Ask Fuzzy system has been developed to address this vital issue which has social and industrial impact. It is an attractive and friendly visual user interface that facilitates expressing queries using both fuzziness and traditional methods. The fuzziness is not expressed explicitly inside the database, it is rather absorbed and effectively handled by an intermediate layer which is cleverly incorporated between the front-end visual user-interface and the back-end database.

Software testing is the most visible and cost-consuming activity in assuring the quality of software systems. In today's large-scale software systems, test (suite) maintenance is an inseparable part of software maintenance. Clarity of the purpose of each test case in the suite can be improved by proper naming and appropriate packaging, which decreases the cost of test maintenance. As a software system evolves its test suites need to be updated (maintained) to verify new or modified functionality of the software, and thus test cases may need to be categorized in a different way. In this work, we are proposing a technique to categorize the test cases automatically based on their coverage information. The proposed process can be performed dynamically over the life cycle of the system to improve the quality of test packaging. We build a social network of test cases and use coverage information to define links between them. This network is used to identify higher groups of test cases such as test package. To the best of our knowledge, this is the first trial in this direction. To evaluate our technique, we applied it on three open source systems with available JUnit test suits to identify test packages. We measured the quality of the discovered packages in terms of cohesion and coupling and compared them with the original packaging from test developers of these projects. The result shows that our technique can be used to categorize test cases automatically by even improving the quality of the packages.

In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in continuously. Therefore, if the model does not learn from the new data, the model may not perform as well with the new data especially when the model's training data is different from the test data. The new framework proposed in this paper rebuilds the classifier model using selected data from test data set to improve the accuracy of the model. A case study on intrusion detection data set has been performed to evaluate our methodology. The result shows that this approach lead to have more accurate classification models over time.

Measurement and detection of redundancy in test suites attempt to achieve test minimization which in turn can help reduce test maintenance costs, and to also ensure the integrity of test cases. Test suite reduction based on coverage information has been discussed in many previous works. However, the applications of such techniques on real test suites and realistic measurements of redundancy have not yet been experimented thoroughly. To address such a need, we formulate in this paper two experimental metrics for coverage-based measurement of test redundancy in the context of JUnit test suites. We then evaluate the approach by measuring the redundancy of four real Java projects. The automated measures are compared with manual redundancy decisions (performed through an inspection by a tester). The results and lessons learned are interesting and somewhat surprising in that besides they show usefulness of coverage information, they present a set of shortcomings (in terms of precision) for the simplistic coverage-based redundancy measurement approach as discussed in the literature. The root-cause analysis of our observations identify several key lessons learned which should help the testing researchers and practitioners in devising better techniques for more precise measurement of test redundancy.