A Survey and Taxonomy of Intent-Based Code Search

2021 ◽  
Vol 9 (1) ◽  
pp. 69-110
Author(s):  
Shailesh Kumar Shivakumar

In this paper, the authors introduce the novel concept of intent-based code search that categorizes code search goals into a hierarchy. They will explore state-of-the-art techniques in source code search covering various tools, techniques, and algorithms related to source code search. They will survey the code search field through the core use cases of code search such as code reusability, code understanding, and code repair. They propose a user intent-based taxonomy based on the code search goals. The code search goal taxonomy is derived based on deep analysis of literature survey of code search, and the taxonomy is validated based on their exclusive developer survey conducted as part of this paper. The code search goal taxonomy is based on logical categorization of code search goals and shared characteristics (query type, expected response, and such) for each of the categories in the taxonomy. The paper also details the latest trends and surveys the code search tools and the implications on tool design.

Proceedings ◽  
2020 ◽  
Vol 64 (1) ◽  
pp. 22
Author(s):  
David Fassbender ◽  
Tatina Minav

For the longest time, valve-controlled, centralized hydraulic systems have been the state-of-the-art technology to actuate heavy-duty mobile machine (HDMM) implements. Due to the typically low energy efficiency of those systems, a high number of promising, more-efficient actuator concepts has been proposed by academia as well as industry over the last decades as potential replacements for valve control—e.g., independent metering, displacement control, different types of electro-hydraulic actuators (EHAs), electro-mechanic actuators, or hydraulic transformers. This paper takes a closer look on specific HDMM applications for these actuator concepts to figure out where which novel concept can be a better alternative to conventional actuator concepts, and where novel concepts might fail to improve. For this purpose, a novel evaluation algorithm for actuator–HDMM matches is developed based on problem aspects that can indicate an unsuitable actuator–HDMM match. To demonstrate the functionality of the match evaluation algorithm, four actuator concepts and four HDMM types are analyzed and rated in order to form 16 potential actuator–HDMM matches that can be evaluated by the novel algorithm. The four actuator concepts comprise a conventional valve-controlled concept and three different types of EHAs. The HDMM types are excavator, wheel loader, backhoe, and telehandler. Finally, the evaluation of the 16 matches results in 16 mismatch values, of which the lowest indicates the “perfect match”. Low mismatch values could be found in general for EHAs in combination with most HDMMs but also for a valve-controlled actuator concept in combination with a backhoe. Furthermore, an analysis of the concept limitations with suggestions for improvement is included.


2021 ◽  
Vol 9 ◽  
pp. 374-390
Author(s):  
Dinesh Raghu ◽  
Nikhil Gupta ◽  
Mausam

Abstract Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.


Author(s):  
Fuqi Cai ◽  
Changjing Wang ◽  
Qing Huang ◽  
Zhengkang Zuo ◽  
Yunyan Liao

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9–60.9[Formula: see text] in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4–11[Formula: see text] in Precision.


2012 ◽  
Vol 38 (5) ◽  
pp. 1069-1087 ◽  
Author(s):  
Collin McMillan ◽  
Mark Grechanik ◽  
Denys Poshyvanyk ◽  
Chen Fu ◽  
Qing Xie

Author(s):  
E. Kakaras ◽  
A. Koumanakos ◽  
A. Doukelis ◽  
D. Giannakopoulos ◽  
Ch. Hatzilau ◽  
...  

Scope of the work presented is to examine and evaluate the state of the art in technological concepts towards the capture and sequestration of CO2 from coal-fired power plants. The discussion is based on the evaluation of a novel concept dealing with the carbonation-calcination process of lime for CO2 capture from coal fired power plants compared to integration of CO2 capture in an Integrated Gasification Combined Cycle power plant. In the novel concept, coal is gasified with steam in the presence of lime. Lime absorbs the CO2 released from the coal, producing limestone. The produced gas can be a low-carbon or even zero-carbon (H2) gas, depending on the ratio of lime added to the process. The produced gas can be used in state-of-the-art combined cycles for electricity generation, producing almost no CO2 emissions or other harmful pollutants. The limestone is regenerated in a second reactor, where pure CO2 is produced, which can be either marketed to industry or sequestered in long term disposal areas. The simulation model of a Combined Cycle power plant, integrating the novel carbonation-calcination process, is based on available data from a typical natural gas fired Combined Cycle power plant. The natural gas fired power plant was adopted to firing with the low-C fuel, maintaining the basic operating characteristics. The performance of the novel concept power plant is compared to that of an IGCC with CO2 removal by means of Selexol absorption. Results from thermodynamic simulation, dealing with the most important features for CO2 reduction, are presented. The operating characteristics, as well as the main figures of the plant energy balances are included. A preliminary economic comparison is also provided, taking into account investment and operating costs, in order to estimate the electricity cost related to the two different technological approaches and the economic constrains towards the potentials for applications are examined. The cycle calculations were performed using the thermodynamic cycle calculation software ENBIPRO (ENergie-BIllanz-PROgram). ENBIPRO is a powerful tool for heat and mass balance calculations, solving complex thermodynamic circuits, calculating the efficiency, and allowing exergetic and exergoeconomic analysis of power plants. The software code models all pieces of equipment that usually appear in power plant installations and can accurately calculate all thermodynamic properties (temperature, pressure, enthalpy) at each node of the thermodynamic circuit, power consumption of each component, flue gas composition etc [1]. The code has proven its validity by accurately simulating a large number of power plants and through comparison of the results with other commercial software.


Author(s):  
Hans-Jörg Happel ◽  
Thomas Schuster ◽  
Peter Szulman

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