Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Generating Accurate Dependencies for Large Software

Dependencies between program elements can reflect the architecture, design, and implementation of a software project. According a industry report, intra- and inter-module dependencies can be a significant source of latent threats to software maintainability in long-term software development, especially when the software has millions of lines of code. This thesis introduces the design and implementation of an accurate and scalable analysis tool that extracts code dependencies from large C/C++ software projects. The tool analyzes both symbol-level and module-level dependencies of a software system and provides an utilization-based dependency model. The accurate dependencies generated by the tool can be provided as the input to other software analysis suits; the results along can help developers identify potential underutilized and inconsistent dependencies in the software. Such information points to potential refactoring opportunities and assists developers with large-scale refactoring tasks.1 yea

Achievements, open problems and challenges for search based software testing

Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda, focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach

A framework for cots software evaluation and selection for COTS mismatches handling and non-functional requirements

The decision to purchase Commercial Off-The-Shelf (COTS) software needs systematic guidelines so that the appropriate COTS software can be selected in order to provide a viable and effective solution to the organizations. However, the existing COTS software evaluation and selection frameworks focus more on functional aspects and do not give adequate attention to accommodate the mismatch between user requirements and COTS software specification, and also integration with non functional requirements of COTS software. Studies have identified that these two criteria are important in COTS software evaluation and selection. Therefore, this study aims to develop a new framework of COTS software evaluation and selection that focuses on handling COTS software mismatches and integrating the nonfunctional requirements. The study is conducted using mixed-mode methodology which involves survey and interview. The study is conducted in four main phases: a survey and interview of 63 organizations to identify COTS software evaluation criteria, development of COTS software evaluation and selection framework using Evaluation Theory, development of a new decision making technique by integrating Analytical Hierarchy Process and Gap Analysis to handle COTS software mismatches, and validation of the practicality and reliability of the proposed COTS software Evaluation and Selection Framework (COTS-ESF) using experts’ review, case studies and yardstick validation. This study has developed the COTS-ESF which consists of five categories of evaluation criteria: Quality, Domain, Architecture, Operational Environment and Vendor Reputation. It also provides a decision making technique and a complete process for performing the evaluation and selection of COTS software. The result of this study shows that the evaluated aspects of the framework are feasible and demonstrate their potential and practicality to be applied in the real environment. The contribution of this study straddles both the research and practical perspectives of software evaluation by improving decision making and providing a systematic guidelines for handling issue in purchasing viable COTS software

Automated Failure Explanation Through Execution Comparison

When fixing a bug in software, developers must build an understanding or explanation of the bug and how the bug flows through a program. The effort that developers must put into building this explanation is costly and laborious. Thus, developers need tools that can assist them in explaining the behavior of bugs. Dynamic slicing is one technique that can effectively show how a bug propagates through an execution up to the point where a program fails. However, dynamic slices are large because they do not just explain the bug itself; they include extra information that explains any observed behavior that might be connected to the bug. Thus, the explanation of the bug is hidden within this other tangentially related information. This dissertation addresses the problem and shows how a failing execution and a correct execution may be compared in order to construct explanations that include only information about what caused the bug. As a result, these automated explanations are significantly more concise than those explanations produced by existing dynamic slicing techniques. To enable the comparison of executions, we develop new techniques for dynamic analyses that identify the commonalities and differences between executions. First, we devise and implement the notion of a point within an execution that may exist across multiple executions. We also note that comparing executions involves comparing the state or variables and their values that exist within the executions at different execution points. Thus, we design an approach for identifying the locations of variables in different executions so that their values may be compared. Leveraging these tools, we design a system for identifying the behaviors within an execution that can be blamed for a bug and that together compose an explanation for the bug. These explanations are up to two orders of magnitude smaller than those produced by existing state of the art techniques. We also examine how different choices of a correct execution for comparison can impact the practicality or potential quality of the explanations produced via our system

Test generation for high coverage with abstraction refinement and coarsening (ARC)

Testing is the main approach used in the software industry to expose failures. Producing thorough test suites is an expensive and error prone task that can greatly benefit from automation. Two challenging problems in test automation are generating test input and evaluating the adequacy of test suites: the first amounts to producing a set of test cases that accurately represent the software behavior, the second requires defining appropriate metrics to evaluate the thoroughness of the testing activities. Structural testing addresses these problems by measuring the amount of code elements that are executed by a test suite. The code elements that are not covered by any execution are natural candidates for generating further test cases, and the measured coverage rate can be used to estimate the thoroughness of the test suite. Several empirical studies show that test suites achieving high coverage rates exhibit a high failure detection ability. However, producing highly covering test suites automatically is hard as certain code elements are executed only under complex conditions while other might be not reachable at all. In this thesis we propose Abstraction Refinement and Coarsening (ARC), a goal oriented technique that combines static and dynamic software analysis to automatically generate test suites with high code coverage. At the core of our approach there is an abstract program model that enables the synergistic application of the different analysis components. In ARC we integrate Dynamic Symbolic Execution (DSE) and abstraction refinement to precisely direct test generation towards the coverage goals and detect infeasible elements. ARC includes a novel coarsening algorithm for improved scalability. We implemented ARC-B, a prototype tool that analyses C programs and produces test suites that achieve high branch coverage. Our experiments show that the approach effectively exploits the synergy between symbolic testing and reachability analysis outperforming state of the art test generation approaches. We evaluated ARC-B on industry relevant software, and exposed previously unknown failures in a safety-critical software component