Decoding the Black Box: Making AI Understandable for Software Engineers

Author: Denis Avetisyan

A new framework aims to shed light on how artificial intelligence models arrive at solutions for coding tasks, offering developers deeper insights into their behavior.

The study demonstrates how feature attribution, specifically through SHAP values, can illuminate the decision-making process within code summarization models, offering insights into which code elements most influence generated summaries.

This paper introduces FeatureSHAP, an explainability framework attributing large language model behavior in software engineering to semantically meaningful code features.

Despite the increasing automation of software engineering tasks through Large Language Models (LLMs), their “black-box” nature hinders adoption in critical domains demanding trust and accountability. This paper, ‘Toward Explaining Large Language Models in Software Engineering Tasks’, addresses this limitation by introducing FeatureSHAP, a novel framework that attributes LLM behavior to semantically meaningful code features. By leveraging Shapley values, FeatureSHAP provides more interpretable and actionable explanations for developers, demonstrating improved fidelity and aiding informed decision-making in tasks like code generation and summarization. Could this represent a crucial step toward realizing truly practical and reliable explainable AI in software engineering?

The Inevitable Rise of Code LLMs: A Pragmatic Look

The landscape of software engineering is undergoing a rapid transformation fueled by recent breakthroughs in Large Language Models. These models, initially celebrated for their natural language processing abilities, now demonstrate an astonishing capacity for code generation, effectively translating human instructions into functional software. This newfound capability isn’t simply about automating repetitive tasks; it extends to assisting developers with complex problem-solving, suggesting code completions, and even generating entire software components. The potential for automation is substantial, promising increased productivity, reduced development costs, and the democratization of software creation by lowering the barrier to entry for aspiring programmers. While challenges remain in ensuring code quality and security, the emergence of code-generating LLMs represents a pivotal moment, suggesting a future where artificial intelligence plays an increasingly integral role in the software development lifecycle.

While scaling general-purpose Large Language Models has yielded impressive results, crafting functional and reliable code demands more than sheer size. Traditional LLMs, trained primarily on natural language, often struggle with the precise syntax and logical coherence required for programming. A simple misplaced semicolon or a misunderstanding of variable scope can render entire code blocks unusable. Consequently, researchers are finding that specialized models – those explicitly designed and trained on vast datasets of code – are crucial for overcoming these hurdles. These Neural Code Models prioritize semantic correctness and syntactic validity, enabling them to generate code that not only reads well but also executes flawlessly, representing a significant step towards true automation in software development.

The escalating demands of modern software development are driving a crucial evolution beyond general-purpose Large Language Models toward specialized Neural Code Models. These models are architected with a distinct focus on the intricacies of programming languages, prioritizing not just statistical likelihood of token sequences, but also syntactic and semantic correctness. Unlike their broader counterparts, Neural Code Models incorporate techniques like abstract syntax tree awareness and dataflow analysis, allowing them to generate code that compiles, executes, and adheres to programming best practices. This targeted approach enables more reliable code completion, automated bug fixing, and even the synthesis of entire software components, promising a substantial leap in automation capabilities for software engineers and a future where coding tasks are increasingly streamlined and efficient.

Feature attribution analysis reveals that darker highlights indicate features with a greater influence on the generated code.

From Completion to Repair: The Practical Applications

Neural Code Models are currently implemented in numerous Integrated Development Environments (IDEs) and code editors to provide real-time code completion suggestions. These models, typically based on transformer architectures and trained on large corpora of publicly available code, analyze the preceding code context to predict subsequent tokens or code snippets. The functionality extends beyond simple keyword completion; models can suggest entire function calls, code blocks, and even complete algorithms based on established coding patterns. Performance is measured by metrics like “Mean Reciprocal Rank” (MRR) and “Recall@K”, indicating the accuracy and relevance of the top K suggestions. This assistance accelerates development speed and reduces the potential for human error, particularly in repetitive coding tasks.

Program Repair, leveraging Neural Code Models, involves the automated identification and rectification of defects in source code. These models are trained on large datasets of erroneous and corrected code, enabling them to learn patterns associated with common bug types. When presented with faulty code, the model analyzes the code’s structure and semantics to pinpoint potential errors, then generates candidate fixes. These fixes are often implemented as code patches, which can be automatically applied or presented to developers for review. The process typically involves techniques like abstract syntax tree (AST) manipulation and code generation, aiming to restore the code’s intended functionality without introducing new issues. Current research focuses on improving the accuracy and scalability of program repair systems, as well as addressing challenges related to complex bug scenarios and ensuring the safety of automatically applied patches.

Code summarization utilizes neural code models to automatically generate natural language descriptions of source code functionality. This process analyzes code structure and semantics to produce concise and human-readable summaries, effectively bridging the gap between code and documentation. The resulting summaries enhance code comprehension, aiding developers in understanding unfamiliar codebases and facilitating more efficient code reviews. This capability directly improves software maintainability by providing readily available explanations of code logic, reducing the cognitive load required for modification and debugging, and ultimately decreasing the time and resources needed for long-term project upkeep.

Participant ratings indicate FeatureSHAP is consistently perceived as both faithful and useful across both code generation and code summarization tasks, as demonstrated by both aggregate scores and individual example evaluations.

Evaluating the Outputs: Metrics and Benchmarks

BigCodeBench and CodeSearchNet are established benchmarks utilized for the objective evaluation of code generation models. BigCodeBench, comprising over 250GB of code data, focuses on multi-turn code completion and complex tasks, while CodeSearchNet offers a collection of code and natural language descriptions, enabling the assessment of code retrieval and generation from documentation. These datasets provide standardized evaluation environments, allowing researchers to compare model performance across various programming languages and task complexities. The large scale of these benchmarks is crucial for robust evaluation, minimizing the impact of dataset bias and ensuring generalizability of results to real-world coding scenarios.

CodeBLEU and BERTScore are automated metrics employed to evaluate the quality of code generated by models. CodeBLEU operates by calculating modified n-gram precision, comparing generated code to a set of reference solutions, and incorporating metrics to penalize length differences and reward matching abstract syntax tree (AST) nodes. BERTScore, conversely, assesses semantic similarity by leveraging contextual embeddings from BERT to compute a similarity score between tokens in the generated and reference code. While CodeBLEU focuses on lexical overlap and syntactic correctness, BERTScore prioritizes semantic equivalence, allowing for variations in code style and structure as long as the underlying meaning is preserved. Both metrics provide quantitative assessments, facilitating comparisons between different code generation models and tracking performance improvements.

Rigorous evaluation of code generation models, including Qwen2.5-Coder and GPT-4, is essential for monitoring development and pinpointing areas needing refinement. Utilizing the FeatureSHAP method for assessing model outputs demonstrates a clear performance advantage over random baselines; FeatureSHAP consistently achieves a Noise Score ranging from 0.011 to 0.021. This represents a substantial improvement when compared to the 0.168 to 0.194 Noise Score attained by random baseline models, indicating FeatureSHAP’s superior ability to identify and quantify feature importance within generated code.

FeatureSHAP consistently achieves lower noise scores than using the LLM directly as an attribution method across both GPT-4.1-Nano and Qwen2.5-Coder-3B-Instruct, indicating more reliable feature importance estimations.

The Quest for Transparency: Explainable AI

To truly trust and refine complex machine learning models, understanding how they arrive at decisions is paramount. Explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) and FeatureSHAP, address this need by moving beyond simply predicting outcomes to illuminating the reasoning behind them. These methods don’t treat the model as a “black box,” but rather assign each input feature a value representing its contribution to the model’s output. By quantifying feature importance, developers gain crucial insights into which variables most influence predictions, allowing for model debugging, bias detection, and improved overall performance. This level of transparency fosters confidence in the model’s reliability and facilitates responsible AI development, moving the field toward systems that are not only accurate but also interpretable and trustworthy.

To move beyond the “black box” nature of complex models, researchers are employing techniques that dissect model outputs and link them to understandable features within the input data. A crucial component of this process is the utilization of Abstract Syntax Trees (ASTs), which represent the code’s hierarchical structure. By parsing code into an AST, these methods can identify semantically meaningful elements – variables, functions, or specific code blocks – and determine their influence on the model’s predictions. This isn’t simply identifying which features are important, but how the code’s inherent organization contributes to the outcome, allowing developers to understand the reasoning behind a model’s decision and build more robust and trustworthy systems.

Beyond attributing predictions to broader features, advancements in Explainable AI now offer granular insights down to the individual code token level with techniques like TokenSHAP. This allows developers to not simply understand which features influence a model, but precisely how specific code elements contribute to its decisions. Rigorous statistical validation, employing a Wilcoxon signed-rank test, confirms the effectiveness of this approach; the resulting p-values, consistently below 0.05, establish a statistically significant difference between FeatureSHAP explanations and those generated by random baselines, bolstering confidence in the accuracy and reliability of these fine-grained attribution methods.

Attribution maps reveal that the model focuses on different levels of detail-individual tokens (top) versus broader features (bottom)-when processing the same input.

Towards Robust Systems: The Future of Automated Software

Recent advancements demonstrate a powerful synergy between neural code models and Explainable AI (XAI) techniques, culminating in automated test case generation. This process moves beyond simply producing functional code; instead, it proactively verifies its correctness through the creation of targeted tests. By leveraging XAI methods, such as FeatureSHAP, these systems can identify crucial code components and generate test cases designed to specifically validate their behavior. This automated approach drastically reduces the manual effort required for software testing, enhances code reliability, and fosters greater confidence in the integrity of automated systems. The ability to automatically generate tests represents a significant step toward building truly robust and trustworthy software, minimizing potential errors and maximizing performance.

Current advancements in automated systems are shifting the focus from mere code production to comprehensive quality assurance, fostering increased confidence in their outputs. Recent studies reveal a strong positive response to this holistic approach, with user evaluations of both code generation and summarization tasks – facilitated by the FeatureSHAP technique – consistently achieving a median satisfaction rating of 4 out of 5. This indicates that automated systems are not only capable of creating functional code, but also of delivering results that meet user expectations regarding clarity and reliability, a critical step towards widespread adoption and trust in intelligent automation.

Ongoing research aims to broaden the applicability of these automated testing and verification techniques beyond current implementations, addressing a diverse spectrum of software engineering hurdles. Demonstrating substantial practical impact, evaluations using the Cliff’s delta ($\delta$) metric reveal large effect sizes, consistently ranging from 0.7 to 0.9 when comparing the FeatureSHAP method against random baselines. These findings suggest that the integration of Neural Code Models with Explainable AI (XAI) not only generates functional code but also establishes a robust foundation for building trustworthy and intelligently automated systems, promising increased reliability and user confidence in future software development.

The pursuit of explainability in large language models, as demonstrated by FeatureSHAP, feels predictably Sisyphean. This paper attempts to map model behavior to ‘semantically meaningful code features’ – a noble effort, but one destined to become another layer of abstraction. Marvin Minsky observed, “You can’t always get what you want.” This resonates deeply; developers crave understanding, yet each explanation inevitably introduces new complexities. The very act of attributing behavior, even with FeatureSHAP’s focus on code features, is a simplification. It’s a temporary reprieve from the inherent opacity, a fragile scaffolding built atop a system that will, inevitably, find new ways to surprise – and break – the elegant theories attempting to contain it. Documentation, naturally, will lag behind the inevitable entropy.

What’s Next?

The pursuit of explainability in large language models applied to software engineering inevitably generates more layers of abstraction. FeatureSHAP, as presented, attributes behavior to “semantically meaningful code features.” One anticipates a future where these features themselves require explanation, spawning FeatureSHAPSHAP, and so on. The core problem isn’t a lack of attribution methods; it’s the assumption that understanding how a model arrived at a conclusion is inherently useful when the underlying logic remains opaque and often empirically derived. It’s a sophisticated debugging process applied before the bug manifests.

The field will likely shift from explaining outputs to predicting failures. Resources currently devoted to post-hoc interpretability may prove more valuable if directed towards robust error detection and preventative measures. This isn’t to say understanding is unimportant, but that a model’s internal state is a moving target. What appears ‘meaningful’ today will be a statistical artifact tomorrow. The challenge isn’t building clearer windows into the black box, but accepting that some boxes simply aren’t meant to be opened without consequence.

Ultimately, the question isn’t whether a model can be explained, but whether the effort yields a return greater than simply rewriting the code by hand. The current trajectory suggests a proliferation of tools promising insight, while the fundamental problem – brittle, statistically-driven automation – remains largely unaddressed. Perhaps the next innovation isn’t explainable AI, but a renewed appreciation for the elegance of well-understood, albeit less ‘intelligent’, systems. The field doesn’t need more microservices – it needs fewer illusions.

Original article: https://arxiv.org/pdf/2512.20328.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/