Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers

Large language models have catalyzed an unprecedented wave in code generation. While achieving significant advances, they blur the distinctions between machine-and human-authored source code, causing integrity and authenticity issues of software artifacts. Previous methods such as DetectGPT have proven effective in discerning machine-generated texts, but they do not identify and harness the unique patterns of machine-generated code. Thus, its applicability falters when applied to code. In this paper, we carefully study the specific patterns that characterize machine and human-authored code. Through a rigorous analysis of code attributes such as length, lexical diversity, and naturalness, we expose unique pat-terns inherent to each source. We particularly notice that the structural segmentation of code is a critical factor in identifying its provenance. Based on our findings, we propose a novel machine-generated code detection method called DetectCodeGPT, which improves DetectGPT by capturing the distinct structural patterns of code. Diverging from conventional techniques that depend on external LLMs for perturbations, DetectCodeGPT perturbs the code corpus by strategically inserting spaces and newlines, ensuring both efficacy and efficiency. Experiment results show that our approach significantly outperforms state-of-the-art techniques in detecting machine-generated code.

Automatic and Efficient Customization of Neural Networks for ML Applications

ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API. However, these APIs offer the same pre-trained models regardless of how their output is used by different applications. This can be suboptimal as not all ML inference errors can cause application failures, and the distinction between inference errors that can or cannot cause failures varies greatly across applications. To tackle this problem, we first study 77 real-world applications, which collectively use six ML APIs from two providers, to reveal common patterns of how ML API output affects applications' decision processes. Inspired by the findings, we propose ChameleonAPI, an optimization framework for ML APIs, which takes effect without changing the application source code. ChameleonAPI provides application developers with a parser that automatically analyzes the application to produce an abstract of its decision process, which is then used to devise an application-specific loss function that only penalizes API output errors critical to the application. ChameleonAPI uses the loss function to efficiently train a neural network model customized for each application and deploys it to serve API invocations from the respective application via existing interface. Compared to a baseline that selects the best-of-all commercial ML API, we show that ChameleonAPI reduces incorrect application decisions by 43%.

Orthogonalized SGD and Nested Architectures for Anytime Neural Networks

We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over time. Efficient architectural designs for these networks focus on re-using internal state; subnetworks must produce representations relevant for both immediate prediction as well as refinement by subsequent network stages. We consider traditional branched networks as well as a new class of recursively nested networks. Our new optimizer, Orthogonalized SGD, dynamically re-balances task-specific gradients when training a multitask network. In the context of anytime architectures, this optimizer projects gradients from later outputs onto a parameter subspace that does not interfere with those from earlier outputs. Experiments demonstrate that training with Orthogonalized SGD significantly improves generalization accuracy of anytime networks.

ALERT: Accurate Anytime Learning for Energy and Timeliness

An increasing number of software applications incorporate runtime Deep Neural Network (DNN) inference for its great accuracy in many problem domains. While much prior work has separately tackled the problems of improving DNN-inference accuracy and improving DNN-inference efficiency, an important problem is under-explored: disciplined methods for dynamically managing application-specific latency, accuracy, and energy tradeoffs and constraints at run time. To address this need, we propose ALERT, a co-designed combination of runtime system and DNN nesting technique. The runtime takes latency, accuracy, and energy constraints, and uses dynamic feedback to predict the best DNN-model and system power-limit setting. The DNN nesting creates a type of flexible network that efficiently delivers a series of results with increasing accuracy as time goes on. These two parts well complement each other: the runtime is aware of the tradeoffs of different DNN settings, and the nested DNNs' flexibility allows the runtime prediction to satisfy application requirements even in unpredictable, changing environments. On real systems for both image and speech, ALERT achieves close-to-optimal results. Comparing with the optimal static DNN-model and power-limit setting, which is impractical to predict, ALERT achieves a harmonic mean 33% of energy savings while satisfying accuracy constraints, and reduces image-classification error rate by 58% and sentence-prediction perplexity by 52% while satisfying energy constraints.