Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

Analysis of Contingency Tables

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

https://dl.acm.org/doi/pdf/10.1145/3212695

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Software Product Line Engineering Foundations Principles And Techniques

Effective debugging is crucial to producing reliable software. Manual debugging is becoming prohibitively expensive, especially due to the growing size and complexity of programs. Given that fault localization is one of the most expensive activities in program debugging, there has been a great demand for fault localization techniques that can help guide programmers to the locations of faults. In this paper, a technique named DStar (D*) is proposed which can suggest suspicious locations for fault localization automatically without requiring any prior information on program structure or semantics. D* is evaluated across 24 programs, and is compared to 38 different fault localization techniques. Both single-fault and multi-fault programs are used. Results indicate that D* is more effective at locating faults than all the other techniques it is compared to. An empirical evaluation is also conducted to illustrate how the effectiveness of D* increases as the exponent * grows, and then levels off when the exponent * exceeds a critical value. Discussions are presented to support such observations.

The DStar Method for Effective Software Fault Localization

Effective debugging is crucial to producing dependable software. Manual debugging is becoming prohibitively expensive, especially due to the growing size and complexity of programs. Given that fault localization is one of the most expensive activities in program debugging, there has been a great demand for fault localization techniques that can help guide programmers to the locations of faults. In this paper a technique named DStar (D*), which has its origins rooted in similarity coefficient-based analysis, is proposed, which can identify suspicious locations for fault localization automatically without requiring any prior information on program structure or semantics. D* is evaluated across 21 programs and is compared to 16 different fault localization techniques. Both single-fault and multi-fault programs are used. Results indicate that D* is more effective at locating faults than all the other techniques it is compared to.

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Software Fault Localization Using DStar (D

In practice, a program may contain multiple bugs. The simultaneous presence of these bugs may deteriorate the effectiveness of existing fault-localization techniques to locate program bugs. While it is acceptable to use all failed and successful tests to identify suspicious code for programs with exactly one bug, it is not appropriate to use the same approach for programs with multiple bugs because the due-to relationship between failed tests and underlying bugs cannot be easily identified. One solution is to generate fault-focused clusters by grouping failed tests caused by the same bug into the same clusters. We propose MSeer—an advanced fault localization technique for locating multiple bugs in parallel. Our major contributions include the use of (1) a revised Kendall tau distance to measure the distance between two failed tests, (2) an innovative approach to simultaneously estimate the number of clusters and assign initial medoids to these clusters, and (3) an improved K-medoids clustering algorithm to better identify the due-to relationship between failed tests and their corresponding bugs. Case studies on 840 multiple-bug versions of seven programs suggest that MSeer performs better in terms of effectiveness and efficiency than two other techniques for locating multiple bugs in parallel.

MSeer—An Advanced Technique for Locating Multiple Bugs in Parallel

In response to the highly competitive market and the pressure to cost-effectively release good-quality software, companies have adopted the concept of software product line to reduce development cost. However, testing and debugging of each product, even from the same family, is still done independently. This can be very expensive. To solve this problem, we need to explore how test cases generated for one product can be used for another product. We propose a genetic algorithm-based framework which integrates software fault localization techniques and focuses on reusing test specifications and input values whenever feasible. Case studies using four software product lines and eight fault localization techniques were conducted to demonstrate the effectiveness of our framework. Discussions on factors that may affect the effectiveness of the proposed framework is also presented. Our results indicate that test cases generated in such a way can be easily reused (with appropriate conversion) between different products of the same family and help reduce the overall testing and debugging cost.

Ruizhi Gao

Papers

A Survey on Software Fault Localization

The DStar Method for Effective Software Fault Localization

Software Fault Localization Using DStar (D

MSeer—An Advanced Technique for Locating Multiple Bugs in Parallel

Genetic Algorithm-based Test Generation for Software Product Line with the Integration of Fault Localization Techniques