An important goal of automatic testing techniques, including random testing is to achieve high code coverage with a minimum set of test cases. To meet this goal, random testing researchers have proposed many techniques to generate test inputs and method call sequences that yield higher code coverage. However, most proposed random testing techniques are only suitable for toy systems, and they achieve low code coverage rates while generating too many unnecessary test cases on large-scale software systems. We propose GENRED, a tool that utilizes three approaches: input on demand creation and coverage-based method selection techniques that enhance Randoop, a state-of-the-art feedback-directed random testing technique, and finally, a sequence-based reduction technique that removes redundant test cases without executing them. We evaluate GENRED as a tool to test four open-source systems. The results show that these techniques achieve branch coverage improvement by 13.7% and prune 51.8% of the test cases without sacrificing code coverage.

Testing object-oriented (OO) software is critical because OO languages are commonly used in developing modern software systems. In testing OO software, one important and yet challenging problem is to generate desirable object instances for receivers and arguments to achieve high code coverage, such as branch coverage, or find bugs. Our initial empirical findings show that coverage of nearly half of the difficult-to-cover branches that a state-of-the-art test-generation tool cannot cover requires desirable object instances that the tool fails to generate. Generating desirable object instances has been a significant challenge for automated test-generation tools, partly because the search space for such desirable object instances is huge, no matter whether these tools compose method sequences to produce object instances or directly construct object instances. To address this significant challenge, we propose a novel approach called Object Capture based Automated Testing (OCAT). OCAT captures object instances dynamically from program executions (e.g., ones from system testing or real use). These captured objects assist an existing automated test-generation tool, such as a random testing tool, to achieve higher code coverage. Afterwards, OCAT mutates collected instances, based on observed not-covered branches. We evaluated OCAT on three open source projects, and our empirical results show that OCAT helps a state-of-the-art random testing tool, Randoop, to achieve high branch coverage: on average 68.5%, with 25.5% improved from only 43.0% achieved by Randoop alone.

In software testing and maintenance activities, the observed faults and bugs are reported in bug report managing systems (BRMS) for further analysis and repair. According to the information provided by bug reports, developers need to find out the location of these faults and fix them. However, bug locating usually involves intensively browsing back and forth through bug reports and software code and thus incurs unpredictable cost of labor and time. Hence, establishing a robust model to efficiently and effectively locate and track faults is crucial to facilitate software testing and maintenance. In our observation, some related bug locations are tightly associated with the implicit links among source files. In this paper, we present an implicit social network model using PageRank to establish a social network graph with the extracted links. When a new bug report arrives, the prediction model provides users with likely bug locations according to the implicit social network graph constructed from the co-cited source files. The proposed approach has been implemented in real-world software archives and can effectively predict correct bug locations.

Changing system requirements, especially for context awareness (CA) systems, often cause modifications in the software systems in order to adapt to dynamic environments. Since the requirements may become temporarily obsolete or contrary to human intentions, the CA systems need to be tuned to resolve the conflict. On the other hand, most CA design methods rely on pre-defined requirements and reasoning engine, thus, fail to address all the possible situations. Consequently, services provided by such a CA system are limited to accommodate some situations and unable to react as expected. Therefore, it is critical for CA systems to capture exceptions at runtime, infer changed human intentions, and adapt to these changes. This study focuses on inference of ever-changing human intentions and monitoring human intentions to handle system evolution. In this paper, we present an inference mechanism of human intentions via the human-machine dimensional inference ontology (HDIO). This ontology gives inference rules based on the BDI logic to deduce human intentions from contexts. Furthermore, the inference exercises of a healthcare system example shows how user intentions relate to system requirements and how they help improve self-adaptability of CA systems.

User intentions to obtain services evolve over time. Since the changes of intention may occur at any time, some system requirements may become temporarily obsolete or contrary to a user intention. However, user intentions often indicate potential, valuable goals for upgrading the system to a new version or engaging new development. Our research tackles requirements analysis issues via feedback from context awareness systems to identify user intentions and capture instant definitions of goal in a robust manner. This paper presents a context-aware goal elicitation process by exploring the aspects of data, information, knowledge and wisdom. Furthermore, captured user intentions and goals are shown in a case study of healthcare system, and issues for the goal elicitation are explored.

Maintaining traceability links among software artifacts is particularly important for many software engineering tasks. Even though automatic traceability link recovery tools are successful in identifying the semantic connections among software artifacts produced during software development, no existing traceability link management approach can effectively and automatically deal with software evolution. We propose a technique to automatically manage traceability link evolution and update the links in evolving software. Our novel technique, called incremental latent semantic indexing (iLSI), allows for the fast and low-cost LSI computation for the update of traceability links by analyzing the changes to software artifacts and by reusing the result from the previous LSI computation before the changes. We present our iLSI technique, and describe a complete automatic traceability link evolution management tool, TLEM, that is capable of interactively and quickly updating traceability links in the presence of evolving software artifacts. We report on our empirical evaluation with various experimental studies to assess the performance and usefulness of our approach.

Bug locating usually involves intensive search activities and incurs unpredictable cost of labor and time. An issue of information retrieval on bug locations is particularly addressed to facilitate identifying bugs from software code. In this paper, a novel bug retrieval approach with co-location shrinkage (CS) is proposed. The proposed approach has been implemented in open-source software projects collected from real-world repositories, and consistently improves the retrieval accuracy of a state-of-the-art Support Vector Machine (SVM) model.