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The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable insights available in unstructured natural-language descriptions in the specifications, which leads to suboptimal test coverage. Recently, to address this gap, researchers have developed techniques that extract rules from these human-readable descriptions and query knowledge bases to derive meaningful input values. However, these techniques are limited in the types of rules they can extract and prone to produce inaccurate results. This paper presents RESTGPT, an innovative approach that leverages the power and intrinsic context-awareness of Large Language Models (LLMs) to improve REST API testing. RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. It then augments the original specification with these rules and values. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation. Given these promising results, we outline future research directions for advancing REST API testing through LLMs., Comment: To be published in the 46th IEEE/ACM International Conference on Software Engineering - New Ideas and Emerging Results Track (ICSE-NIER 2024)

Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from a potentially large set of parameters, and sampling values from the virtually infinite parameter input space. Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally (i.e., not considering their importance or complexity) and lacking prioritization strategies. Furthermore, these tools struggle when response schemas are absent in the specification or exhibit variants. To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize operations and parameters during exploration. Our approach dynamically analyzes request and response data to inform dependent parameters and adopts a sampling-based strategy for efficient processing of dynamic API feedback. We evaluated our technique on ten RESTful services, comparing it against state-of-the-art REST testing tools with respect to code coverage achieved, requests generated, operations covered, and service failures triggered. Additionally, we performed an ablation study on prioritization, dynamic feedback analysis, and sampling to assess their individual effects. Our findings demonstrate that our approach outperforms existing REST API testing tools in terms of effectiveness, efficiency, and fault-finding ability., Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023)

Code translation aims to convert source code from one programming language (PL) to another. Given the promising abilities of large language models (LLMs) in code synthesis, researchers are exploring their potential to automate code translation. The prerequisite for advancing the state of LLM-based code translation is to understand their promises and limitations over existing techniques. To that end, we present a large-scale empirical study to investigate the ability of general LLMs and code LLMs for code translation across pairs of different languages, including C, C++, Go, Java, and Python. Our study, which involves the translation of 1,700 code samples from three benchmarks and two real-world projects, reveals that LLMs are yet to be reliably used to automate code translation -- with correct translations ranging from 2.1% to 47.3% for the studied LLMs. Further manual investigation of unsuccessful translations identifies 15 categories of translation bugs. We also compare LLM-based code translation with traditional non-LLM-based approaches. Our analysis shows that these two classes of techniques have their own strengths and weaknesses. Finally, insights from our study suggest that providing more context to LLMs during translation can help them produce better results. To that end, we propose a prompt-crafting approach based on the symptoms of erroneous translations; this improves the performance of LLM-based code translation by 5.5% on average. Our study is the first of its kind, in terms of scale and breadth, that provides insights into the current limitations of LLMs in code translation and opportunities for improving them. Our dataset -- consisting of 1,700 code samples in five PLs with 10K+ tests, 43K+ translated code, 1,748 manually labeled bugs, and 1,365 bug-fix pairs -- can help drive research in this area., Comment: Published in ICSE 2024

Modern web applications make extensive use of API calls to update the UI state in response to user events or server-side changes. For such applications, API-level testing can play an important role, in-between unit-level testing and UI-level (or end-to-end) testing. Existing API testing tools require API specifications (e.g., OpenAPI), which often may not be available or, when available, be inconsistent with the API implementation, thus limiting the applicability of automated API testing to web applications. In this paper, we present an approach that leverages UI testing to enable API-level testing for web applications. Our technique navigates the web application under test and automatically generates an API-level test suite, along with an OpenAPI specification that describes the application's server-side APIs (for REST-based web applications). A key element of our solution is a dynamic approach for inferring API endpoints with path parameters via UI navigation and directed API probing. We evaluated the technique for its accuracy in inferring API specifications and the effectiveness of the "carved" API tests. Our results on seven open-source web applications show that the technique achieves 98% precision and 56% recall in inferring endpoints. The carved API tests, when added to test suites generated by two automated REST API testing tools, increase statement coverage by 52% and 29% and branch coverage by 99% and 75%, on average. The main benefits of our technique are: (1) it enables API-level testing of web applications in cases where existing API testing tools are inapplicable and (2) it creates API-level test suites that cover server-side code efficiently while exercising APIs as they would be invoked from an application's web UI, and that can augment existing API test suites.

Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore next. These methods are challenged by sparsity of high-fitness samples in the training set, a problem that has been in the literature. A less recognized but equally important problem stems from the distribution of training samples in the design space: leading methods are not designed for scenarios where the desired optimum is in a region that is not only poorly represented in training data, but also relatively far from the highly represented low-fitness regions. We show that this problem of "separation" in the design space is a significant bottleneck in existing model-based optimization tools and propose a new approach that uses a novel VAE as its search model to overcome the problem. We demonstrate its advantage over prior methods in robustly finding improved samples, regardless of the imbalance and separation between low- and high-fitness samples. Our comprehensive benchmark on real and semi-synthetic protein datasets as well as solution design for physics-informed neural networks, showcases the generality of our approach in discrete and continuous design spaces. Our implementation is available at https://github.com/sabagh1994/PGVAE.

The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on another, under certain assumptions. We leverage this insight to implement a new tool, CIMLA, for discovering condition-dependent changes in causal relationships. We then use CIMLA to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data sets, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Finally, we employ CIMLA to analyze a previously published single-cell RNA-seq data set collected from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.

Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that generate test cases for API endpoints using various strategies. Understanding how these techniques compare to one another is difficult, as they have been evaluated on different benchmarks and using different metrics. To fill this gap, we performed an empirical study aimed to understand the landscape in automated testing of REST APIs and guide future research in this area. We first identified, through a systematic selection process, a set of 10 state-of-the-art REST API testing tools that included tools developed by both researchers and practitioners. We then applied these tools to a benchmark of 20 real-world open-source RESTful services and analyzed their performance in terms of code coverage achieved and unique failures triggered. This analysis allowed us to identify strengths, weaknesses, and limitations of the tools considered and of their underlying strategies, as well as implications of our findings for future research in this area., Comment: 13 pages, 6 figures, In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2022

We present a DevIce-to-System Performance EvaLuation (DISPEL) workflow that integrates transistor and interconnect modeling, parasitic extraction, standard cell library characterization, logic synthesis, cell placement and routing, and timing analysis to evaluate system-level performance of new CMOS technologies. As the impact of parasitic resistances and capacitances continues to increase with dimensional downscaling, component-level optimization alone becomes insufficient, calling for a holistic assessment and optimization methodology across the boundaries between devices, interconnects, circuits, and systems. The physical implementation flow in DISPEL enables realistic analysis of complex wires and vias in VLSI systems and their impact on the chip power, speed, and area, which simple circuit simulations cannot capture. To demonstrate the use of DISPEL, a 32-bit commercial processor core is implemented using theoretical n-type MoS2 and p-type Black Phosphorous (BP) planar FETs at a projected 5-nm node, and the performance is benchmarked against Si FinFETs. While the superior gate control of the MoS2/BP FETs can theoretically provide 51% reduction in the iso-frequency energy consumption, the actual performance can be greatly limited by the source/drain contact resistances. With the large amount of data generated by DISPEL, a neural-network is trained to predict the key performance metrics of the 32-bit processor core using the characteristics of transistors and interconnects as the input features without the need to go through the time-consuming physical implementation flow. The machine learning algorithms show great potentials as a means for evaluation and optimization of new CMOS technologies and identifying the most significant technology design parameters., Comment: 12 pages, 23 figures

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool's efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.

Energetic electrons inside Earth's outer Van Allen belt pose a major radiation threat to space-borne electronics that often play vital roles in our modern society. Ultra-relativistic electrons with energies greater than or equal to two Megaelectron-volt (MeV) are of particular interest due to their high penetrating ability, and thus forecasting these >=2 MeV electron levels has significant meaning to all space sectors. Here we update the latest development of the predictive model for MeV electrons inside the Earth's outer radiation belt. The new version, called PreMevE-2E, focuses on forecasting ultra-relativistic electron flux distributions across the outer radiation belt, with no need of in-situ measurements except for at the geosynchronous (GEO) orbit. Model inputs include precipitating electrons observed in low-Earth-orbits by NOAA satellites, upstream solar wind conditions (speeds and densities) from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated a total of 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and also successfully tested ensemble forecasting by using groups of top-performing models. All models are individually trained, validated, and tested by in-situ electron data from NASA's Van Allen Probes mission. It is shown that the final ensemble model generally outperforms individual models overs L-shells, and this PreMevE-2E model provides reliable and high-fidelity 25-hr (~1-day) and 50-hr (~2-day) forecasts with high mean performance efficiency values. Our results also suggest this new model is dominated by non-linear components at low L-shells (< ~4) for ultra-relativistic electrons, which is different from the dominance of linear components at all L-shells for 1 MeV electrons as previously discovered., Comment: manuscript submitted to Space Weather journal

3D integration technologies are seeing widespread adoption in the semiconductor industry to offset the limitations and slowdown of two-dimensional scaling. High-density 3D integration techniques such as face-to-face wafer bonding with sub-10 $\mu$m pitch can enable new ways of designing SoCs using all 3 dimensions, like folding a microprocessor design across multiple 3D tiers. However, overlapping thermal hotspots can be a challenge in such 3D stacked designs due to a general increase in power density. In this work, we perform a thorough thermal simulation study on sign-off quality physical design implementation of a state-of-the-art, high-performance, out-of-order microprocessor on a 7nm process technology. The physical design of the microprocessor is partitioned and implemented in a 2-tier, 3D stacked configuration with logic blocks and memory instances in separate tiers (logic-over-memory 3D). The thermal simulation model was calibrated to temperature measurement data from a high-performance, CPU-based 2D SoC chip fabricated on the same 7nm process technology. Thermal profiles of different 3D configurations under various workload conditions are simulated and compared. We find that stacking microprocessor designs in 3D without considering thermal implications can result in maximum die temperature up to 12{\deg}C higher than their 2D counterparts under the worst-case power-indicative workload. This increase in temperature would reduce the amount of time for which a power-intensive workload can be run before throttling is required. However, logic-over-memory partitioned 3D CPU implementation can mitigate this temperature increase by half, which makes the temperature of the 3D design only 6$^\circ$C higher than the 2D baseline. We conclude that using thermal aware design partitioning and improved cooling techniques can overcome the thermal challenges associated with 3D stacking.

3D integration, i.e., stacking of integrated circuit layers using parallel or sequential processing is gaining rapid industry adoption with the slowdown of Moore's law scaling. 3D stacking promises potential gains in performance, power and cost but the actual magnitude of gains varies depending on end-application, technology choices and design. In this talk, we will discuss some key challenges associated with 3D design and how design-for-3D will require us to break traditional silos of micro-architecture, circuit/physical design and manufacturing technology to work across abstractions to enable the gains promised by 3D technologies., Comment: 5 pages, 6 Figures, invited talk at S3S conference held in San Jose, October 2019