Pub Date : 2019-01-22DOI: 10.1109/ICSE-NIER.2019.00021
Michele Tufano, Hitesh Sajnani, Kim Herzig
The pervasive adoption of Continuous Integration practices – both in industry and open source projects – has led software building to become a daily activity for thousands of developers around the world. Companies such as Microsoft have invested in in-house infrastructures with the goal of optimizing the build process. CloudBuild, a distributed and caching build service developed internally by Microsoft, runs the build process in parallel in the cloud and relies on caching to accelerate builds. This allows for agile development and rapid delivery of software even several times a day. However, moving towards faster builds requires not only improvements on the infrastructure side, but also attention to developers' changes in the software. Surely, architectural decisions and software changes, such as addition of dependencies, can lead to significant build time increase. Yet, estimating the impact of such changes on build time can be challenging when dealing with complex, distributed, and cached build systems. In this paper, we envision a predictive model able to preemptively alert developers on the extent to which their software changes may impact future building activities. In particular, we describe an approach that analyzes the developer's change and predicts (i) whether it impacts (any of) the Longest Critical Path; (ii) may lead to build time increase and its delta; and (iii) the percentage of future builds that might be affected by such change.
{"title":"Towards Predicting the Impact of Software Changes on Building Activities","authors":"Michele Tufano, Hitesh Sajnani, Kim Herzig","doi":"10.1109/ICSE-NIER.2019.00021","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00021","url":null,"abstract":"The pervasive adoption of Continuous Integration practices – both in industry and open source projects – has led software building to become a daily activity for thousands of developers around the world. Companies such as Microsoft have invested in in-house infrastructures with the goal of optimizing the build process. CloudBuild, a distributed and caching build service developed internally by Microsoft, runs the build process in parallel in the cloud and relies on caching to accelerate builds. This allows for agile development and rapid delivery of software even several times a day. However, moving towards faster builds requires not only improvements on the infrastructure side, but also attention to developers' changes in the software. Surely, architectural decisions and software changes, such as addition of dependencies, can lead to significant build time increase. Yet, estimating the impact of such changes on build time can be challenging when dealing with complex, distributed, and cached build systems. In this paper, we envision a predictive model able to preemptively alert developers on the extent to which their software changes may impact future building activities. In particular, we describe an approach that analyzes the developer's change and predicts (i) whether it impacts (any of) the Longest Critical Path; (ii) may lead to build time increase and its delta; and (iii) the percentage of future builds that might be affected by such change.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121378557","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}
Pub Date : 2018-12-27DOI: 10.1109/ICSE-NIER.2019.00019
K. Dam, T. Tran, J. Grundy, A. Ghose, Yasutaka Kamei
The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects, e.g. customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.
{"title":"Towards Effective AI-Powered Agile Project Management","authors":"K. Dam, T. Tran, J. Grundy, A. Ghose, Yasutaka Kamei","doi":"10.1109/ICSE-NIER.2019.00019","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00019","url":null,"abstract":"The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects, e.g. customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126608397","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}
Pub Date : 2018-12-21DOI: 10.1109/ICSE-NIER.2019.00023
A. Miranskyy, Lei Zhang
A quantum computer (QC) can solve many computational problems more efficiently than a classic one. The field of QCs is growing: companies (such as D-Wave, IBM, Google, and Microsoft) are building QC offerings. We position that software engineers should look into defining a set of software engineering practices that apply to QC's software. To start this process, we give examples of challenges associated with testing such software and sketch potential solutions to some of these challenges.
{"title":"On Testing Quantum Programs","authors":"A. Miranskyy, Lei Zhang","doi":"10.1109/ICSE-NIER.2019.00023","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00023","url":null,"abstract":"A quantum computer (QC) can solve many computational problems more efficiently than a classic one. The field of QCs is growing: companies (such as D-Wave, IBM, Google, and Microsoft) are building QC offerings. We position that software engineers should look into defining a set of software engineering practices that apply to QC's software. To start this process, we give examples of challenges associated with testing such software and sketch potential solutions to some of these challenges.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114645754","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}
Pub Date : 2018-07-26DOI: 10.1109/ICSE-NIER.2019.00010
Marcel Böhme
As researchers, we already understand how to make testing more effective and efficient at finding bugs. However, as fuzzing (i.e., automated testing) becomes more widely adopted in practice, practitioners are asking: Which assurances does a fuzzing campaign provide that exposes no bugs? When is it safe to stop the fuzzer with a reasonable residual risk? How much longer should the fuzzer be run to achieve sufficient coverage? It is time for us to move beyond the innovation of increasingly sophisticated testing techniques, to build a body of knowledge around the explication and quantification of the testing process, and to develop sound methodologies to estimate and extrapolate these quantities with measurable accuracy. In our vision of the future practitioners leverage a rich statistical toolset to assess residual risk, to obtain statistical guarantees, and to analyze the cost-benefit trade-off for ongoing fuzzing campaigns. We propose a general framework as a first starting point to tackle this fundamental challenge and discuss a large number of concrete opportunities for future research.
{"title":"Assurances in Software Testing: A Roadmap","authors":"Marcel Böhme","doi":"10.1109/ICSE-NIER.2019.00010","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00010","url":null,"abstract":"As researchers, we already understand how to make testing more effective and efficient at finding bugs. However, as fuzzing (i.e., automated testing) becomes more widely adopted in practice, practitioners are asking: Which assurances does a fuzzing campaign provide that exposes no bugs? When is it safe to stop the fuzzer with a reasonable residual risk? How much longer should the fuzzer be run to achieve sufficient coverage? It is time for us to move beyond the innovation of increasingly sophisticated testing techniques, to build a body of knowledge around the explication and quantification of the testing process, and to develop sound methodologies to estimate and extrapolate these quantities with measurable accuracy. In our vision of the future practitioners leverage a rich statistical toolset to assess residual risk, to obtain statistical guarantees, and to analyze the cost-benefit trade-off for ongoing fuzzing campaigns. We propose a general framework as a first starting point to tackle this fundamental challenge and discuss a large number of concrete opportunities for future research.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800188","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}
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