1,125 research outputs found
Code Coverage Measurement and Fault Localization Approaches
Code coverage measurement plays an important role in white-box testing, both in industrial practice and academic research. Several areas are highly dependent on code coverage as well, including test case generation, test prioritization, fault localization, and others. Out of these areas, this dissertation focuses on two main topics, and the thesis points are divided into two parts accordingly. The first part consists of one thesis point that discusses the differences between methods for measuring code coverage in Java and the effects of these differences. The second part focuses on a fault localization technique called spectrum-based fault localization that utilizes code coverage to estimate the risk of each program element being faulty. More specifically, the corresponding two thesis points are discussing the improvement of the efficiency of spectrum-based approaches by incorporating external information, e.g., usersâ knowledge, and context data extracted from call chains
Using contextual knowledge in interactive fault localization
Tool support for automated fault localization in program debugging is limited because state-of-the-art algorithms often fail to provide efficient help to the user. They usually offer a ranked list of suspicious code elements, but the fault is not guaranteed to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) â which uses code coverage information of test cases and their execution outcomes to calculate the ranks â, the developer has to investigate several locations before finding the faulty code element. Yet, all the knowledge she a priori has or acquires during this process is not reused by the SBFL tool. There are existing approaches in which the developer interacts with the SBFL algorithm by giving feedback on the elements of the prioritized list. We propose a new approach called iFL which extends interactive approaches by exploiting contextual knowledge of the user about the next item in the ranked list (e. g., a statement), with which larger code entities (e. g., a whole function) can be repositioned in their suspiciousness. We implemented a closely related algorithm proposed by Gong et al. , called Talk . First, we evaluated iFL using simulated users, and compared the results to SBFL and Talk . Next, we introduced two types of imperfections in the simulation: userâs knowledge and confidence levels. On SIR and Defects4J, results showed notable improvements in fault localization efficiency, even with strong user imperfections. We then empirically evaluated the effectiveness of the approach with real users in two sets of experiments: a quantitative evaluation of the successfulness of using iFL , and a qualitative evaluation of practical uses of the approach with experienced developers in think-aloud sessions
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
Code intelligence leverages machine learning techniques to extract knowledge
from extensive code corpora, with the aim of developing intelligent tools to
improve the quality and productivity of computer programming. Currently, there
is already a thriving research community focusing on code intelligence, with
efforts ranging from software engineering, machine learning, data mining,
natural language processing, and programming languages. In this paper, we
conduct a comprehensive literature review on deep learning for code
intelligence, from the aspects of code representation learning, deep learning
techniques, and application tasks. We also benchmark several state-of-the-art
neural models for code intelligence, and provide an open-source toolkit
tailored for the rapid prototyping of deep-learning-based code intelligence
models. In particular, we inspect the existing code intelligence models under
the basis of code representation learning, and provide a comprehensive overview
to enhance comprehension of the present state of code intelligence.
Furthermore, we publicly release the source code and data resources to provide
the community with a ready-to-use benchmark, which can facilitate the
evaluation and comparison of existing and future code intelligence models
(https://xcodemind.github.io). At last, we also point out several challenging
and promising directions for future research
A Systematic Review of Automated Query Reformulations in Source Code Search
Fixing software bugs and adding new features are two of the major maintenance
tasks. Software bugs and features are reported as change requests. Developers
consult these requests and often choose a few keywords from them as an ad hoc
query. Then they execute the query with a search engine to find the exact
locations within software code that need to be changed. Unfortunately, even
experienced developers often fail to choose appropriate queries, which leads to
costly trials and errors during a code search. Over the years, many studies
attempt to reformulate the ad hoc queries from developers to support them. In
this systematic literature review, we carefully select 70 primary studies on
query reformulations from 2,970 candidate studies, perform an in-depth
qualitative analysis (e.g., Grounded Theory), and then answer seven research
questions with major findings. First, to date, eight major methodologies (e.g.,
term weighting, term co-occurrence analysis, thesaurus lookup) have been
adopted to reformulate queries. Second, the existing studies suffer from
several major limitations (e.g., lack of generalizability, vocabulary mismatch
problem, subjective bias) that might prevent their wide adoption. Finally, we
discuss the best practices and future opportunities to advance the state of
research in search query reformulations.Comment: 81 pages, accepted at TOSE
Tools and Algorithms for the Construction and Analysis of Systems
This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems
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