Software fault characteristics: A synthesis of the literature

Faults continue to be a significant problem in software. Understanding the nature of these faults is important for practitioners and researchers. There are many published fault characteristics schemes but no one scheme dominates. Consequently it is difficult for practitioners to effectively evaluate the nature of faults in their software systems, and it is difficult for researchers to compare the types of faults found by different fault detection techniques. In this paper we synthesise previous fault characteristics schemes into one comprehensive scheme. Our scheme provides a richer view of faults than the previous schemes published and presents a comprehensive, unified approach which accommodates the many previous schemes. A characteristics-based view of faults should be considered by future researchers in the analysis of software faults and in the design and evaluation of new fault detection tools. We recommend that our fault characteristics scheme be used as a benchmark scheme

Murphy tools: Utilizing extracted gui models for industrial software testing

Abstract-One of the main challenges in adopting modelbased testing (MBT) is the effort and expertise required to produce the formal models. For an existing system, there are various approaches to automate the process of creating the models. In this paper, we share our experiences from a long term industrial evaluation on automatically extracting models of graphical user interface (GUI) applications and utilizing the extracted models to automate and support GUI testing. While model extraction and GUI testing has been recently a popular research topic, most proposed approaches have limitations on what can be modeled and industry adoption has been lacking. We describe the process of using Murphy tools to extract GUI models and utilize these models to automate and support various testing activities. During the evaluation, test engineers of an industrial software company used Murphy tools to support their daily efforts in testing commercial software products during 1 year time period. The results from the evaluation were promising, significantly reducing time and effort required for GUI testing

Automated blackbox GUI specifications enhancement and test data generation

Applications with a Graphical User Interface (GUI) front-end are ubiquitous nowadays. While automated model-based approaches have been shown to be effective in testing of such applications, most existing techniques produce many infeasible event sequences used as GUI test cases. This happens primarily because the behavioral specifications of the GUI under test are ignored. In this dissertation we present an automated framework that reveals an important set of state-based constraints among GUI events based on infeasible (i.e., unexecutable or partially executable) test cases of a GUI test suite. GUIDiVa, an iterative algorithm at the core of our framework, enumerates all possible constraint violations as potential reasons for test case failure, on the failed event of an infeasible test case. It then selects and adds the most promising constraints of each iteration to a final set based on the Validity Weight of constraints. The results of empirical studies on both seeded and nine non-trivial open-source study subjects show that our framework is capable of capturing important aspects of GUI behavior in the form of state-based event constraints, while considerably reducing the number of insfeasible test cases. The second part of this dissertation deals with the problem of automatic generation of relevant test data for parameterized GUI events (i.e., events associated with widgets that accept user inputs such as textboxes and textareas). Current techniques either manipulate the source code of the application under test (AUT) to generate the test data, or blindly use a set of random string values. We propose a novel way to generate the test data by exploiting the information provided in the GUI structure to extract a set of key identifiers for each parameterized GUI widget. These identifiers are used to compose appropriate online search phrases and collect relevant test data from the Internet. The results of an empirical study on five GUI-based applications show that the proposed approach is applicable and results in execution of some hard-to-cover branches in the subject programs. The proposed technique works from a black-box perspective and is entirely independent from GUI modeling and event sequence generation, thus it does not require source code access and offers the possibility of being integrated with existing GUI testing frameworks

A corpus-based study of academic-collocation use and patterns in postgraduate Computer Science students’ writing

Collocation has been considered a problematic area for L2 learners. Various studies have been conducted to investigate native speakers’ (NS) and non-native speakers’ (NNS) use of different types of collocations (e.g., Durrant and Schmitt, 2009; Laufer and Waldman, 2011).These studies have indicated that, unlike NS, NNS rely on a limited set of collocations and tend to overuse them. This raises the question: if NNS tend to overuse a limited set of collocations in their academic writing, would their use of academic collocations in a specific discipline (Computer Science in this study) vary from that of NS and expert writers? This study has three main aims. First, it investigates the use of lexical academic collocations in NNS and NS Computer Science students’ MSc dissertations and compares their uses with those by expert writers in their writing of published research articles. Second, it explores the factors behind the over/underuse of the 24shared lexical collocations among corpora. Third, it develops awareness-raising activities that could be used to help non-expert NNS students with collocation over/underuse problems. For this purpose, a corpus of 600,000 words was compiled from 55 dissertations (26 written by NS and 29 by NNS). For comparison purposes, a reference corpus of 600,269 words was compiled from 63 research articles from prestigious high impact factor Computer Science academic journals. The Academic Word List (AWL) (Coxhead, 2000) was used to develop lists of the most frequent academic words in the student corpora, whose collocations were examined. Quantitative analysis was then carried out by comparing the 100 most frequent noun and verb collocations from each of the student corpora with the reference corpus. The results reveal that both NNS (52%) and NS (78%) students overuse noun collocations compared to the expert writers in the reference corpus. They underuse only a small number of noun collocations (8%). Surprisingly, neither NNS nor NS students significantly over/underused verb collocations compared to the reference corpus. In order to achieve the second aim, mixed methods approach was adopted. First, the variant patterns of the 24 shared noun collocations between NNS and NS corpora were identified to determine whether over/underuse of these collocations could be explained by their differences in the number of patterns used. Approximately half of the 24 collocations identified for their patterns were using more patterns including (Noun + preposition +Noun and Noun + adjective +Noun) that were rarely located in the writing of experts. Second, a categorisation judgement task and semi-structured interviews were carried out with three Computer Scientists to elicit their views on the various factors likely influencing noun collocation choices by the writers across the corpora. Results demonstrate that three main factors could explain the variation: sub-discipline, topic, and genre. To achieve the third pedagogical aim, a sample of awareness-raising activities was designed for the problematic over/underuse of some noun collocations. Using the corpus-based Data Driven Learning (DDL)approach (Johns,1991), three types of awareness-raising activities were developed: noticing collocation, noticing and identifying different patterns of the same collocation, and comparing and contrasting patterns between NNS students’ corpora and the reference corpus. Results of this study suggest that academic collocation use in an ESP context (Computer Science) is related to other factors than students’ lack of knowledge of collocations. Expertness, genre variation, topic and discipline-specific collocations are proved important factors to be considered in ESP. Thus, ESP teachers have to alert their students to the effect of these factors in academic collocation use in subject specific disciplines. This has tangible implications for Applied Linguistics and for teaching practices