Causal Reasoning in Software Quality Assurance: A Systematic Review

• URL: http://arxiv.org/abs/2408.17183v2
• Date: Thu, 10 Oct 2024 11:00:02 GMT
• Title: Causal Reasoning in Software Quality Assurance: A Systematic Review
• Authors: Luca Giamattei, Antonio Guerriero, Roberto Pietrantuono, Stefano Russo,
• Abstract summary: This study provides a systematic review of the scientific literature on causal reasoning for SQA. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain. tools to favour their application are appearing at a fast pace - most of them after 2021.
• Score: 11.887059800587672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining increasing interest as a methodology to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies. Objective: Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities. Methods: A systematic review of the scientific literature on causal reasoning for SQA. The study has found, classified, and analyzed 86 articles, according to established guidelines for software engineering secondary studies. Results: Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl's graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favour their application are appearing at a fast pace - most of them after 2021. Conclusions: The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like ...

Related papers

• KaPQA: Knowledge-Augmented Product Question-Answering [59.096607961704656]
  We introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products. We also propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task.
  arXiv  Detail & Related papers  (2024-07-22T22:14:56Z)
• A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
  We devise three novel text-based tasks for situational reasoning in the traffic domain. We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work. We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
  arXiv  Detail & Related papers  (2023-06-05T01:01:12Z)
• The Quantum Frontier of Software Engineering: A Systematic Mapping Study [16.93115872272979]
  Quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. This paper presents a systematic mapping study of the current state of QSE research.
  arXiv  Detail & Related papers  (2023-05-31T09:26:10Z)
• Utilizing Background Knowledge for Robust Reasoning over Traffic Situations [63.45021731775964]
  We focus on a complementary research aspect of Intelligent Transportation: traffic understanding. We scope our study to text-based methods and datasets given the abundant commonsense knowledge. We adopt three knowledge-driven approaches for zero-shot QA over traffic situations.
  arXiv  Detail & Related papers  (2022-12-04T09:17:24Z)
• Better Retrieval May Not Lead to Better Question Answering [59.1892787017522]
  A popular approach to improve the system's performance is to improve the quality of the retrieved context from the IR stage. We show that for StrategyQA, a challenging open-domain QA dataset that requires multi-hop reasoning, this common approach is surprisingly ineffective.
  arXiv  Detail & Related papers  (2022-05-07T16:59:38Z)
• Contingency-Aware Influence Maximization: A Reinforcement Learning Approach [52.109536198330126]
  influence (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms.
  arXiv  Detail & Related papers  (2021-06-13T16:42:22Z)
• Machine Learning Techniques for Software Quality Assurance: A Survey [5.33024001730262]
  We discuss various approaches in both fault prediction and test case prioritization. Recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features.
  arXiv  Detail & Related papers  (2021-04-29T00:37:27Z)
• SQAPlanner: Generating Data-InformedSoftware Quality Improvement Plans [10.032135866890384]
  We develop and evaluate an information visualization for our SQAPlanner approach. We find that 80% of our survey respondents perceived that our visualization is more actionable.
  arXiv  Detail & Related papers  (2021-02-19T00:17:28Z)
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [76.20963684020145]
  Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. Bizarre approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning.
  arXiv  Detail & Related papers  (2020-11-12T06:41:05Z)
• Using Unsupervised Learning to Help Discover the Causal Graph [0.76146285961466]
  AitiaExplorer is an exploratory causal analysis tool which uses unsupervised learning for feature selection. A problem statement and requirements for the software are outlined. It is found that AitiaExplorer meets these requirements and is a useful exploratory causal analysis tool.
  arXiv  Detail & Related papers  (2020-09-22T20:07:19Z)
• A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming [3.963609604649393]
  A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. This paper is under consideration for acceptance in TPLP.
  arXiv  Detail & Related papers  (2020-08-04T13:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.