Cloud Programming Languages and Infrastructure from Code: An Empirical Study

被引:0
作者
Simhandl, Georg [1 ]
Zdun, Uwe [1 ]
机构
[1] Univ Vienna, Fac Comp Sci, Res Grp Software Architecture, Vienna, Austria
来源
PROCEEDINGS OF THE 17TH ACM SIGPLAN INTERNATIONAL CONFERENCE ON SOFTWARE LANGUAGE ENGINEERING, SLE 2024 | 2024年
基金
奥地利科学基金会;
关键词
Programming Language; Cloud; Infrastructure From Code; Empirical Study; Experiment;
D O I
10.1145/3687997.3695643
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Infrastructure-from-Code (IfC) is a new approach to DevOps and an advancement of Infrastructure-as-Code (IaC). One of its key concepts is to provide a higher level of abstraction facilitated by new programming languages or software development kits, which automatically generate the necessary code and configurations to provision the infrastructure, deploy the application, andmanage the cloud services. IfC approaches promise higher developer productivity by reducing DevOps-specific tasks and the expert knowledge required. However, empirical studies on developers' performance, perceived ease of use, and usability related to IfC are missing. We conducted a controlled experiment (n=40) to assess the usability of the cloud programming languages (PL) and software development kits (SDK). Both approaches involve similar effectiveness. We found that the PL-based approach was moderately less efficient but increased correctness with time spent on programming. Tracing generated infrastructure configurations from code was more challenging with the SDK-based approach. Applying thematic analysis, 19 themes emerged related to usability barriers, supporting factors, security, cloud cost, and enhancement areas. We conclude with five findings and future directions.
引用
收藏
页码:143 / 156
页数:14
相关论文
共 25 条
  • [1] Amazon Web Services Inc., 2024, Infrastructure As Code Provisioning Tool-AWS CloudFormation
  • [2] AmptWeb Services Inc., 2024, The most efficient way to get things done in the cloud
  • [3] Infrastructure From Code: The Next Generation of Cloud Lifecycle Automation
    Aviv, Itzhak
    Gafni, Ruti
    Sherman, Sofia
    Aviv, Berta
    Sterkin, Asher
    Bega, Etzik
    [J]. IEEE SOFTWARE, 2023, 40 (01) : 42 - 49
  • [4] Aviv Itzhak, 2023, EUR C SOFTW ARCH ECS
  • [5] A Penny a Function: Towards Cost Transparent Cloud Programming
    Boehme, Lukas
    Beckmann, Tom
    Baltes, Sebastian
    Hirschfeld, Robert
    [J]. PROCEEDINGS OF THE 2ND ACM SIGPLAN INTERNATIONAL WORKSHOP ON PROGRAMMING ABSTRACTIONS AND INTERACTIVE NOTATIONS, TOOLS, AND ENVIRONMENTS, PAINT 2023, 2023, : 1 - 10
  • [6] Braun V., 2012, Qualitative psychology: A practical guide to research methods, P57, DOI [DOI 10.1037/13620-004, 10.1080/17439760.2016.1262613, DOI 10.1080/17439760.2016.1262613]
  • [7] Developing a New DevOps Modelling Language to Support the Creation of Infrastructure as Code
    Chiari, Michele
    Di Nitto, Elisabetta
    Mucientes, Adrian Noguero
    Xiang, Bin
    [J]. ADVANCES IN SERVICE-ORIENTED AND CLOUD COMPUTING, ESOCC 2022, 2022, 1617 : 88 - 93
  • [8] Answering ordinal questions with ordinal data using ordinal statistics
    Cliff, N
    [J]. MULTIVARIATE BEHAVIORAL RESEARCH, 1996, 31 (03) : 331 - 350
  • [9] Recommended Steps for Thematic Synthesis in Software Engineering
    Cruzes, Daniela S.
    Dyba, Tore
    [J]. 2011 FIFTH INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2011), 2011, : 275 - 284
  • [10] Encoretivity AB, 2024, Development Platform for typesafe distributed systems