Reducing Test Suite of State-Sensitivity Partitioning (SSP)

Software testing is one of the most vital phases of software development lifecycle that aims to detect software faults. Test case generation dominates the software testing research. SSP is one of many techniques proposed for test case generation. The goal of SSP is to avoid exhaustively testing all possible combinations of inputs and preconditions. The test cases produced by SSP are formed of a sequence of events. For instance, a queue test case might encompass the addition of thirty items onto the queue; deletion of three items, addition of sixty more items, seven deletions and examining the outcome. Notwithstanding perceiving the finite bounds of the queue size, there is an endless engage of sequences along with no upper limit on the sequence’s length. Therefore, the sequence might get lengthy as a result of comprising data states that are redundant. The test suite size is expanded due to the data states redundancies and subsequently, the testing process will become insufficient. Thus, it is a necessity to optimize the SSP test suite by removing the redundant data states. This paper addresses the issue of SSP suite reduction, which part of the process for optimizing test suite produced by the SSP

ExceLint: Automatically Finding Spreadsheet Formula Errors

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.Comment: Appeared at OOPSLA 201

A systematic mapping study on cross-project defect prediction

Cross-Project-Defect Prediction as a sub-topic of defect prediction in general has become a popular topic in research. In this article, we present a systematic mapping study with the focus on CPDP, for which we found 50 publications. We summarize the approaches presented by each publication and discuss the case study setups and results. We discovered a great amount of heterogeneity in the way case studies are conducted, because of differences in the data sets, classifiers, performance metrics, and baseline comparisons used. Due to this, we could not compare the results of our review on a qualitative basis, i.e., determine which approaches perform best for CPDP.Comment: Under Revie