Locating bugs without looking back

Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history

Establishing Multilevel Test-to-Code Traceability Links

Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCtracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCtracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCtracer using four large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 78% and test-class-to-class links with an MAP of 93%

Improving Information Retrieval Bug Localisation Using Contextual Heuristics

Software developers working on unfamiliar systems are challenged to identify where and how high-level concepts are implemented in the source code prior to performing maintenance tasks. Bug localisation is a core program comprehension activity in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source files as the documents to be retrieved, ranked by relevance. Current approaches rely on project history, in particular previously fixed bugs and versions of the source code. Existing IR techniques fall short of providing adequate solutions in finding all the source code files relevant for a bug. Without additional help, bug localisation can become a tedious, time- consuming and error-prone task. My research contributes a novel algorithm that, given a bug report and the application’s source files, uses a combination of lexical and structural information to suggest, in a ranked order, files that may have to be changed to resolve the reported bug without requiring past code and similar reports. I study eight applications for which I had access to the user guide, the source code, and some bug reports. I compare the relative importance and the occurrence of the domain concepts in the project artefacts and measure the effectiveness of using only concept key words to locate files relevant for a bug compared to using all the words of a bug report. Measuring my approach against six others, using their five metrics and eight projects, I position an effected file in the top-1, top-5 and top-10 ranks on average for 44%, 69% and 76% of the bug reports respectively. This is an improvement of 23%, 16% and 11% respectively over the best performing current state-of-the-art tool. Finally, I evaluate my algorithm with a range of industrial applications in user studies, and found that it is superior to simple string search, as often performed by developers. These results show the applicability of my approach to software projects without history and offers a simpler light-weight solution

Combining business process and failure modelling to increase yield in electronics manufacturing

The prediction and capturing of defects in low-volume assembly of electronics is a technical challenge that is a prerequisite for design for manufacturing (DfM) and business process improvement (BPI) to increase first-time yields and reduce production costs. Failures at the component-level (component defects) and system-level (such as defects in design and manufacturing) have not been incorporated in combined prediction models. BPI efforts should have predictive capability while supporting flexible production and changes in business models. This research was aimed at the integration of enterprise modelling (EM) and failure models (FM) to support business decision making by predicting system-level defects. An enhanced business modelling approach which provides a set of accessible failure models at a given business process level is presented in this article. This model-driven approach allows the evaluation of product and process performance and hence feedback to design and manufacturing activities hence improving first-time yield and product quality. A case in low-volume, high-complexity electronics assembly industry shows how the approach leverages standard modelling techniques and facilitates the understanding of the causes of poor manufacturing performance using a set of surface mount technology (SMT) process failure models. A prototype application tool was developed and tested in a collaborator site to evaluate the integration of business process models with the execution entities, such as software tools, business database, and simulation engines. The proposed concept was tested for the defect data collection and prediction in the described case study

Large Language Models for Software Engineering: Survey and Open Problems

This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio

Information Retrieval and Spectrum Based Bug Localization: Better Together

Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual infor-mation in bug reports, while spectrum-based techniques pro-cess program spectra (i.e., a record of which program el-ements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only con-sider one source of information, either bug reports or pro-gram spectra, which is not optimal. To deal with the limita-tion of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature loca-tion methods that are adapted for bug localization. Experi-ments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80 % in terms of number of bugs successfully localized when a developer in

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation