Towards a Science of Causal Interpretability in Deep Learning for Software Engineering

Abstract: This dissertation addresses achieving causal interpretability in Deep Learning for Software Engineering (DL4SE). While Neural Code Models (NCMs) show strong performance in automating software tasks, their lack of transparency in causal relationships between inputs and outputs limits full understanding of their capabilities. To build trust in NCMs, researchers and practitioners must explain code predictions. Associational interpretability, which identifies correlations, is often insufficient for tasks requiring intervention and change analysis. To address this, the dissertation introduces DoCode, a novel post hoc interpretability method for NCMs. DoCode uses causal inference to provide programming language-oriented explanations of model predictions. It follows a four-step pipeline: modeling causal problems using Structural Causal Models (SCMs), identifying the causal estimand, estimating effects with metrics like Average Treatment Effect (ATE), and refuting effect estimates. Its framework is extensible, with an example that reduces spurious correlations by grounding explanations in programming language properties. A case study on deep code generation across interpretability scenarios and various deep learning architectures demonstrates DoCode's benefits. Results show NCMs' sensitivity to code syntax changes and their ability to learn certain programming concepts while minimizing confounding bias. The dissertation also examines associational interpretability as a foundation, analyzing software information's causal nature using tools like COMET and TraceXplainer for traceability. It highlights the need to identify code confounders and offers practical guidelines for applying causal interpretability to NCMs, contributing to more trustworthy AI in software engineering.

Overview of Causal Interpretability in Deep Learning for Software Engineering

The academic paper entitled "Towards a Science of Causal Interpretability in Deep Learning for Software Engineering" presents a novel interpretability approach, namely do$_{code$, designed specifically to provide insights into the decision-making process of Neural Code Models (NCMs). The research addresses the inherent lack of transparency in these models' predictions when applied to various software engineering tasks. The primary focus of the paper is on establishing causality rather than mere correlations in model predictions, which is critical for trustworthiness in automated systems.

Motivation and Methodology

Current mechanisms to interpret NCMs often rely on associational methods, where performance metrics like accuracy are analyzed, but without further understanding of why models make certain predictions. This is insufficient for applications requiring interventions and understanding the impact of changes. To overcome these limitations, the paper introduces a structured methodology leveraging causal inference to extract programming language-oriented explanations of model behavior.

Do$_{code consists of a formal four-step process:

1. Modeling the Causal Problem: Structural Causal Models (SCMs) provide graphical representations of assumptions about the causal relationships among variables involved in code prediction. This includes defining potential outcomes, interventions, and confounders in the context of software engineering.

2. Identifying Causal Estimands: Using graph-based algorithms and do-calculus, potential paths for identifying causal effects are drawn. Techniques like back-door criterion and instrumental variables help in formulating correct mathematical expressions to encode causal relationships.

3. Estimating Causal Effects: The framework employs statistical and machine learning estimation methods adapted to the nature of the variables (binary, discrete, continuous) to assess the causal impact on model predictions. Average Treatment Effect (ATE) offers a quantifiable measure of these impacts.

4. Refuting Effect Estimates: Validity of the causal estimates is ensured using refutation techniques involving perturbations. Methods like placing unobserved common causes or placebo treatments test robustness and sensitivity of causal effects.

Results and Findings

The research progresses through various interpretability scenarios utilizing the do$_{code framework, focusing on interventions in model data and hyper-parameters. This includes scenarios like assessing the impact of buggy versus fixed code, documentation presence, and syntactic alterations using clone methods.

Across multiple configurations and scenarios, distinct causal effects are discerned. For instance, the presence of a high correlation suggests spurious relationships due to unaccounted confounders. Where interventions like masking AST node types showed no significant changes, indicating a lack of causal connection between feature alteration and performance shifts.

Key findings from the study reveal:

• NCMs may not fully capture structural information of programming languages, reflected in scenarios where random token masking impacts model performance more than grammar-based interventions.
• Spurious correlations exist, where associational dependences do not imply causation, underscoring the necessity for causal analysis.
• Do$_{code's strategies are actionable for Software Engineering, wherein causal interpretability yields tangible insights into model biases and effectiveness, facilitating further refinement and trustworthiness improvements.

Implications for Future Developments

The implications of this paper are substantial for both theoretical advancement and practical applications in AI and Software Engineering. The formal framework and practical considerations delineated in do$_{code potentially transform how researchers and practitioners approach NCM interpretability. By proposing a shift from associational to causal interpretability, future developments in AI can focus on establishing trust and reliability in automated systems, crucial for deployment in real-world settings.

Moreover, these findings emphasize the need to consider not only the surface performance metrics but delve deeper into the causative factors underpinning model predictions. This helps in drawing more rigorous evaluations and making informed decisions about using NCMs in software engineering endeavors. As the authors illustrate, understanding the 'why' of model performance enhances the ability to predict changes and make adaptations, anchoring the role of causal interpretability in advancing Software Engineering research.

Ultimately, this work contributes a stepping stone toward establishing a robust science of causal interpretability, setting precedents for designing trustworthy AI tools in Software Engineering with rigorous empirical backing.