2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)最新文献_第2页

ICSE-NIER 2023 Committee Lists ICSE-NIER 2023委员会名单

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-05-01 DOI: 10.1109/icse-nier58687.2023.00006

引用次数: 0

Test-Driven Development Benefits Beyond Design Quality: Flow State and Developer Experience 测试驱动开发的好处超越了设计质量:流程状态和开发人员经验

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-05-01 DOI: 10.1109/ICSE-NIER58687.2023.00025

Pedro Henrique Calais Guerra, Lissa Franzini

{"title":"Test-Driven Development Benefits Beyond Design Quality: Flow State and Developer Experience","authors":"Pedro Henrique Calais Guerra, Lissa Franzini","doi":"10.1109/ICSE-NIER58687.2023.00025","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00025","url":null,"abstract":"Test-driven development (TDD) is a coding technique that combines design and testing in an iterative and incremental fashion. It prescribes that tests written before the production code help the developer to find good interfaces and to evolve the design safely and incrementally. Improvements on the design of code produced by the test-driven development approach have been extensively evaluated in the literature; in this research, we focus on seeking explanations on the benefits of TDD in another dimension which we believe has been undervalued – developer experience. We identified that there is a natural connection between the TDD approach and flow state, a well-known mental state characterized by total immersion, focus, and involvement in a task that promotes increased enjoyment and productivity. We present evidence that the continuous stream of mini-scope, short-lived, red-green-refactor cycles of TDD frame the development task as a structure that creates the pre-conditions reported by neuroscience research to produce flow state, namely (1) clear goals, (2) unambiguous feedback, (3) challenge-skill balance and (4) sense of control. Our work contributes to increase the understanding on the reasons why adopting practices such as TDD can benefit the software development process as a whole and can support its adoption in software development projects.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125104600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assurance Case Development as Data: A Manifesto 保证案例开发作为数据:一个宣言

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-05-01 DOI: 10.1109/ICSE-NIER58687.2023.00030

C. Menghi, Torin Viger, Alessio Di Sandro, Chris Rees, Jeff Joyce, M. Chechik

引用次数: 0

A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems 一种新颖实用的自动驾驶系统环内验证场景建模框架

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-05-01 DOI: 10.1109/ICSE-NIER58687.2023.00021

Dehui Du, Bo Li, Chenghang Zheng, Xinyuan Zhang

{"title":"A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems","authors":"Dehui Du, Bo Li, Chenghang Zheng, Xinyuan Zhang","doi":"10.1109/ICSE-NIER58687.2023.00021","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00021","url":null,"abstract":"Scenario modeling for Autonomous Driving Systems (ADS) enables scenario-based simulation and verification which are critical for the development of safe ADS. However, with the increasing complexity and uncertainty of ADS, it becomes increasingly challenging to manually model driving scenarios and conduct verification analysis. To tackle these challenges, we propose a novel and pragmatic framework for scenario modeling, simulation and verification. The novelty is that it’s a verification-in-the-loop scenario modeling framework. The scenario modeling language with formal semantics is proposed based on the domain knowledge of ADS. It facilitates scenario verification to analyze the safety of scenario models. Moreover, the scenario simulation is implemented based on the scenario executor. Compared with existing works, our framework can simplify the description of scenarios in a non-programming, user-friendly manner, model stochastic behavior of vehicles, support safe verification of scenario models with UPPAAL-SMC and generate executable scenario in some open-source simulators such as CARLA. To preliminarily demonstrate the effectiveness and feasibility of our approach, we build a prototype tool and apply our approach in several typical scenarios for ADS.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks 判断亚当:ML4SE任务优化方法性能研究

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-03-06 DOI: 10.1109/ICSE-NIER58687.2023.00027

D. Pasechnyuk, Anton Prazdnichnykh, Mikhail Evtikhiev, T. Bryksin

{"title":"Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks","authors":"D. Pasechnyuk, Anton Prazdnichnykh, Mikhail Evtikhiev, T. Bryksin","doi":"10.1109/ICSE-NIER58687.2023.00027","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00027","url":null,"abstract":"Solving a problem with a deep learning model requires researchers to optimize the loss function with a certain optimization method. The research community has developed more than a hundred different optimizers, yet there is scarce data on optimizer performance in various tasks. In particular, none of the benchmarks test the performance of optimizers on source code-related problems. However, existing benchmark data indicates that certain optimizers may be more efficient for particular domains. In this work, we test the performance of various optimizers on deep learning models for source code and find that the choice of an optimizer can have a significant impact on the model quality, with up to two-fold score differences between some of the relatively well-performing optimizers. We also find that RAdam optimizer (and its modification with the Lookahead envelope) is the best optimizer that almost always performs well on the tasks we consider. Our findings show a need for a more extensive study of the optimizers in code-related tasks, and indicate that the ML4SE community should consider using RAdam instead of Adam as the default optimizer for code-related deep learning tasks.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"33 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121097903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities MLTEing模型:协商、评估和记录模型和系统质量

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-03-03 DOI: 10.1109/ICSE-NIER58687.2023.00012

Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain J. Cruickshank, G. Lewis, Christian Kästner

引用次数: 2

Iterative Assessment and Improvement of DNN Operational Accuracy DNN运算精度的迭代评估与改进

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-03-02 DOI: 10.1109/ICSE-NIER58687.2023.00014

Antonio Guerriero, R. Pietrantuono, S. Russo

{"title":"Iterative Assessment and Improvement of DNN Operational Accuracy","authors":"Antonio Guerriero, R. Pietrantuono, S. Russo","doi":"10.1109/ICSE-NIER58687.2023.00014","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00014","url":null,"abstract":"Deep Neural Networks (DNN) are nowadays largely adopted in many application domains thanks to their human-like, or even superhuman, performance in specific tasks. However, due to unpredictable/unconsidered operating conditions, unexpected failures show up on field, making the performance of a DNN in operation very different from the one estimated prior to release.In the life cycle of DNN systems, the assessment of accuracy is typically addressed in two ways: offline, via sampling of operational inputs, or online, via pseudo-oracles. The former is considered more expensive due to the need for manual labeling of the sampled inputs. The latter is automatic but less accurate.We believe that emerging iterative industrial-strength life cycle models for Machine Learning systems, like MLOps, offer the possibility to leverage inputs observed in operation not only to provide faithful estimates of a DNN accuracy, but also to improve it through remodeling/retraining actions.We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines \"low-cost\" online pseudo-oracles and \"high-cost\" offline sampling techniques to estimate and improve the operational accuracy of a DNN in the iterations of its life cycle. Preliminary results show the benefits of combining the two approaches and integrating them in the DNN life cycle.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133630377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reasoning-Based Software Testing 基于推理的软件测试

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-03-02 DOI: 10.1109/ICSE-NIER58687.2023.00018

L. Giamattei, R. Pietrantuono, S. Russo

{"title":"Reasoning-Based Software Testing","authors":"L. Giamattei, R. Pietrantuono, S. Russo","doi":"10.1109/ICSE-NIER58687.2023.00018","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00018","url":null,"abstract":"With software systems becoming increasingly pervasive and autonomous, our ability to test for their quality is severely challenged. Many systems are called to operate in uncertain and highly-changing environment, not rarely required to make intelligent decisions by themselves. This easily results in an intractable state space to explore at testing time. The state-of-the-art techniques try to keep the pace, e.g., by augmenting the tester’s intuition with some form of (explicit or implicit) learning from observations to search this space efficiently. For instance, they exploit historical data to drive the search (e.g., ML-driven testing) or the tests execution data itself (e.g., adaptive or search-based testing). Despite the indubitable advances, the need for smartening the search in such a huge space keeps to be pressing.We introduce Reasoning-Based Software Testing (RBST), a new way of thinking at the testing problem as a causal reasoning task. Compared to mere intuition-based or state-of-the-art learning-based strategies, we claim that causal reasoning more naturally emulates the process that a human would do to \"smartly\" search the space. RBST aims to mimic and amplify, with the power of computation, this ability. The conceptual leap can pave the ground to a new trend of techniques, which can be variously instantiated from the proposed framework, by exploiting the numerous tools for causal discovery and inference. Preliminary results reported in this paper are promising.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130533619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Safe-DS: A Domain Specific Language to Make Data Science Safe Safe- ds:一种使数据科学安全的领域特定语言

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-02-28 DOI: 10.1109/ICSE-NIER58687.2023.00019

Lars Reimann, Günter Kniesel-Wünsche

{"title":"Safe-DS: A Domain Specific Language to Make Data Science Safe","authors":"Lars Reimann, Günter Kniesel-Wünsche","doi":"10.1109/ICSE-NIER58687.2023.00019","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00019","url":null,"abstract":"Due to the long runtime of Data Science (DS) pipelines, even small programming mistakes can be very costly, if they are not detected statically. However, even basic static type checking of DS pipelines is difficult because most are written in Python. Static typing is available in Python only via external linters. These require static type annotations for parameters or results of functions, which many DS libraries do not provide.In this paper, we show how the wealth of Python DS libraries can be used in a statically safe way via Safe-DS, a domain specific language (DSL) for DS. Safe-DS catches conventional type errors plus errors related to range restrictions, data manipulation, and call order of functions, going well beyond the abilities of current Python linters. Python libraries are integrated into Safe-DS via a stub language for specifying the interface of its declarations, and an API-Editor that is able to extract type information from the code and documentation of Python libraries, and automatically generate suitable stubs.Moreover, Safe-DS complements textual DS pipelines with a graphical representation that eases safe development by preventing syntax errors. The seamless synchronization of textual and graphic view lets developers always choose the one best suited for their skills and current task.We think that Safe-DS can make DS development easier, faster, and more reliable, significantly reducing development costs.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132810300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Alternative to Cells for Selective Execution of Data Science Pipelines 选择性执行数据科学管道的替代单元

2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) Pub Date : 2023-02-28 DOI: 10.1109/ICSE-NIER58687.2023.00029

Lars Reimann, Günter Kniesel-Wünsche

{"title":"An Alternative to Cells for Selective Execution of Data Science Pipelines","authors":"Lars Reimann, Günter Kniesel-Wünsche","doi":"10.1109/ICSE-NIER58687.2023.00029","DOIUrl":"https://doi.org/10.1109/ICSE-NIER58687.2023.00029","url":null,"abstract":"Data Scientists often use notebooks to develop Data Science (DS) pipelines, particularly since they allow to selectively execute parts of the pipeline. However, notebooks for DS have many well-known flaws. We focus on the following ones in this paper: (1) Notebooks can become littered with code cells that are not part of the main DS pipeline but exist solely to make decisions (e.g. listing the columns of a tabular dataset). (2) While users are allowed to execute cells in any order, not every ordering is correct, because a cell can depend on declarations from other cells. (3) After making changes to a cell, this cell and all cells that depend on changed declarations must be rerun. (4) Changes to external values necessitate partial re-execution of the notebook. (5) Since cells are the smallest unit of execution, code that is unaffected by changes, can inadvertently be re-executed.To solve these issues, we propose to replace cells as the basis for the selective execution of DS pipelines. Instead, we suggest populating a context-menu for variables with actions fitting their type (like listing columns if the variable is a tabular dataset). These actions are executed based on a data-flow analysis to ensure dependencies between variables are respected and results are updated properly after changes. Our solution separates pipeline code from decision making code and automates dependency management, thus reducing clutter and the risk of making errors.","PeriodicalId":297025,"journal":{"name":"2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124391764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0