Software fault prediction is an important and beneficial practice for improving software quality and reliability. The ability to predict which components in a large software system are most likely to contain the largest numbers of faults in the next release helps to better manage projects, including early estimation of possible release delays, and affordably guide corrective actions to improve the quality of the software. However, developing robust fault prediction models is a challenging task and many techniques have been proposed in the literature. Traditional software fault prediction studies mainly focus on manually designing features (e.g. complexity metrics), which are input into machine learning classifiers to identify defective code. However, these features often fail to capture the semantic and structural information of programs. Such information is needed for building accurate fault prediction models. In this survey, we discuss various approaches in fault prediction, also explaining how in recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features and make accurate predictions.
{"title":"Deep Learning for Software Defect Prediction: A Survey","authors":"Safa Omri, C. Sinz","doi":"10.1145/3387940.3391463","DOIUrl":"https://doi.org/10.1145/3387940.3391463","url":null,"abstract":"Software fault prediction is an important and beneficial practice for improving software quality and reliability. The ability to predict which components in a large software system are most likely to contain the largest numbers of faults in the next release helps to better manage projects, including early estimation of possible release delays, and affordably guide corrective actions to improve the quality of the software. However, developing robust fault prediction models is a challenging task and many techniques have been proposed in the literature. Traditional software fault prediction studies mainly focus on manually designing features (e.g. complexity metrics), which are input into machine learning classifiers to identify defective code. However, these features often fail to capture the semantic and structural information of programs. Such information is needed for building accurate fault prediction models. In this survey, we discuss various approaches in fault prediction, also explaining how in recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features and make accurate predictions.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115841855","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}
Cyrine Zid, D. Humeniuk, Foutse Khomh, G. Antoniol
Testing heterogeneous IoT applications such as a home automation systems integrating a variety of devices poses serious challenges. Oftentimes requirements are vaguely defined. Consumer grade cyber-physical devices and software may not meet the reliability and quality standard needed. Plus, system behavior may partially depend on various environmental conditions. For example, WI-FI congestion may cause packet delay; meanwhile cold weather may cause an unexpected drop of inside temperature. We surmise that generating and executing failure exposing scenarios is especially challenging. Modeling phenomenons such as network traffic or weather conditions is complex. One possible solution is to rely on machine learning models approximating the reality. These models, integrated in a system model, can be used to define surrogate models and fitness functions to steer the search in the direction of failure inducing scenarios. However, these models also should be validated. Therefore, there should be a double loop co-evolution between machine learned surrogate models functions and fitness functions. Overall, we argue that in such complex cyber-physical systems, co-evolution and multi-hybrid approaches are needed.
{"title":"Double Cycle Hybrid Testing of Hybrid Distributed IoT System","authors":"Cyrine Zid, D. Humeniuk, Foutse Khomh, G. Antoniol","doi":"10.1145/3387940.3392218","DOIUrl":"https://doi.org/10.1145/3387940.3392218","url":null,"abstract":"Testing heterogeneous IoT applications such as a home automation systems integrating a variety of devices poses serious challenges. Oftentimes requirements are vaguely defined. Consumer grade cyber-physical devices and software may not meet the reliability and quality standard needed. Plus, system behavior may partially depend on various environmental conditions. For example, WI-FI congestion may cause packet delay; meanwhile cold weather may cause an unexpected drop of inside temperature. We surmise that generating and executing failure exposing scenarios is especially challenging. Modeling phenomenons such as network traffic or weather conditions is complex. One possible solution is to rely on machine learning models approximating the reality. These models, integrated in a system model, can be used to define surrogate models and fitness functions to steer the search in the direction of failure inducing scenarios. However, these models also should be validated. Therefore, there should be a double loop co-evolution between machine learned surrogate models functions and fitness functions. Overall, we argue that in such complex cyber-physical systems, co-evolution and multi-hybrid approaches are needed.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121914500","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}
M. D. Stefano, Fabiano Pecorelli, D. Tamburri, Fabio Palomba, A. D. Lucia
Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success may not only depend on source code quality, but also on other aspects like the balance of distance, culture, global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrent mixes of organizational or social structure types, and smells, i.e., sub-optimal patterns across the organizational structure of a software development community that may be precursors of some sort of social debt. We exploit association rule mining to discover frequent relations between them. Our findings show that different organizational patterns are connected to different forms of socio-technical problems, possibly suggesting that practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells depending on the organization of the project.
{"title":"Splicing Community Patterns and Smells: A Preliminary Study","authors":"M. D. Stefano, Fabiano Pecorelli, D. Tamburri, Fabio Palomba, A. D. Lucia","doi":"10.1145/3387940.3392204","DOIUrl":"https://doi.org/10.1145/3387940.3392204","url":null,"abstract":"Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success may not only depend on source code quality, but also on other aspects like the balance of distance, culture, global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrent mixes of organizational or social structure types, and smells, i.e., sub-optimal patterns across the organizational structure of a software development community that may be precursors of some sort of social debt. We exploit association rule mining to discover frequent relations between them. Our findings show that different organizational patterns are connected to different forms of socio-technical problems, possibly suggesting that practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells depending on the organization of the project.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109093","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}
Nicolas Nascimento, Alan R. Santos, Afonso Sales, R. Chanin
Software development practices which enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), a development method which proposes software to be developed focusing primarily on its expected behavior. In this context, this paper investigates how BDD impacts agile software development teams. In order to achieve this, we have conducted a case study on a mobile application development environment which develops software using agile. In total, 42 interviews were performed. Our results indicate that BDD can have positive impacts, increasing collaboration among team members, and negative impacts, like difficulties in writing unit tests. We concluded that BDD has more positive than negative outcomes.
{"title":"Behavior-Driven Development: A case study on its impacts on agile development teams","authors":"Nicolas Nascimento, Alan R. Santos, Afonso Sales, R. Chanin","doi":"10.1145/3387940.3391480","DOIUrl":"https://doi.org/10.1145/3387940.3391480","url":null,"abstract":"Software development practices which enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), a development method which proposes software to be developed focusing primarily on its expected behavior. In this context, this paper investigates how BDD impacts agile software development teams. In order to achieve this, we have conducted a case study on a mobile application development environment which develops software using agile. In total, 42 interviews were performed. Our results indicate that BDD can have positive impacts, increasing collaboration among team members, and negative impacts, like difficulties in writing unit tests. We concluded that BDD has more positive than negative outcomes.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464019","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}
R. Bose, Kapil Singi, Vikrant S. Kaulgud, Sanjay Podder, Adam P. Burden
Modern software applications are becoming ubiquitous and pervasive affecting various aspects of our lives and livelihoods. At the same time, the risks to which these systems expose the organizations and end users are also growing dramatically. Governments and regulatory bodies are moving towards developing laws, regulations, and guidelines for several software applications (e.g., those that use data, are based on AI/ML etc.) across different domains. These mandates impose several challenges in the way how software is built and delivered, primary amongst them is to ensure that software and its delivery processes are compliant. There is a need for governance frameworks that enable the recording, monitoring, and analysis of various activities throughout the application development life cycle making the development processes transparent, traceable, verifiable, auditable, and adhering to regulations and best practices, thereby enabling trustworthiness of software. In this paper, we discuss about the challenges and opportunities of software engineering in the governance era.
{"title":"Software Engineering in a Governed World: Opportunities and Challenges","authors":"R. Bose, Kapil Singi, Vikrant S. Kaulgud, Sanjay Podder, Adam P. Burden","doi":"10.1145/3387940.3392270","DOIUrl":"https://doi.org/10.1145/3387940.3392270","url":null,"abstract":"Modern software applications are becoming ubiquitous and pervasive affecting various aspects of our lives and livelihoods. At the same time, the risks to which these systems expose the organizations and end users are also growing dramatically. Governments and regulatory bodies are moving towards developing laws, regulations, and guidelines for several software applications (e.g., those that use data, are based on AI/ML etc.) across different domains. These mandates impose several challenges in the way how software is built and delivered, primary amongst them is to ensure that software and its delivery processes are compliant. There is a need for governance frameworks that enable the recording, monitoring, and analysis of various activities throughout the application development life cycle making the development processes transparent, traceable, verifiable, auditable, and adhering to regulations and best practices, thereby enabling trustworthiness of software. In this paper, we discuss about the challenges and opportunities of software engineering in the governance era.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116815410","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}
While Search-Based Software Testing (SBST) has improved significantly in the last decade we propose that more flexible, probabilistic models can be leveraged to improve it further. Rather than searching for an individual, or even sets of, test case(s) or datum(s) that fulfil specific needs the goal can be to learn a generative model tuned to output a useful family of values. Such generative models can naturally be decomposed into a structured generator and a probabilistic model that determines how to make non-deterministic choices during generation. While the former constrains the generation process to produce valid values the latter allows learning and tuning to specific goals. SBST techniques differ in their level of integration of the two but, regardless of how close it is, we argue that the flexibility and power of the probabilistic model will be a main determinant of success. In this short paper, we present how some existing SBST techniques can be viewed from this perspective and then propose additional techniques for flexible generative modelling the community should consider. In particular, Probabilistic Programming languages (PPLs) and Genetic Programming (GP) should be investigated since they allow for very flexible probabilistic modelling. Benefits could range from utilising the multiple program executions that SBST techniques typically require to allowing the encoding of high-level test strategies.
{"title":"Flexible Probabilistic Modeling for Search Based Test Data Generation","authors":"R. Feldt, S. Yoo","doi":"10.1145/3387940.3392215","DOIUrl":"https://doi.org/10.1145/3387940.3392215","url":null,"abstract":"While Search-Based Software Testing (SBST) has improved significantly in the last decade we propose that more flexible, probabilistic models can be leveraged to improve it further. Rather than searching for an individual, or even sets of, test case(s) or datum(s) that fulfil specific needs the goal can be to learn a generative model tuned to output a useful family of values. Such generative models can naturally be decomposed into a structured generator and a probabilistic model that determines how to make non-deterministic choices during generation. While the former constrains the generation process to produce valid values the latter allows learning and tuning to specific goals. SBST techniques differ in their level of integration of the two but, regardless of how close it is, we argue that the flexibility and power of the probabilistic model will be a main determinant of success. In this short paper, we present how some existing SBST techniques can be viewed from this perspective and then propose additional techniques for flexible generative modelling the community should consider. In particular, Probabilistic Programming languages (PPLs) and Genetic Programming (GP) should be investigated since they allow for very flexible probabilistic modelling. Benefits could range from utilising the multiple program executions that SBST techniques typically require to allowing the encoding of high-level test strategies.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115284707","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}
Despite the proliferation of software ecosystems (SECOs), growing a sustainable and healthy SECO remains a significant challenge. One approach to mitigate this challenge is the utilization of a mechanism that collects feedback from distributors (distros) and end-users of the SECO releases. This presentation aims at investigating the effectiveness of the feedback mechanism implemented by OpenStack to address the needs of end-users and distros. I mined the OpenStack repositories and mapped 20 distros' bug-related activities. Results suggest that OpenStack releases are actively maintained for 18 months before reaching end-of-life (EOL), which makes coordination with distros difficult because distros usually provide services to their end-users for a period between 36 - 60 months before reaching EOL. Also, bugs are fixed faster by the distros (7 - 76 days) than the OpenStack community (average of 4 months). However, only 22% of the bugs addressed by OpenStack distros are pushed back upstream.
{"title":"Cross-distribution Feedback in Software Ecosystems","authors":"A. Foundjem","doi":"10.1145/3387940.3392188","DOIUrl":"https://doi.org/10.1145/3387940.3392188","url":null,"abstract":"Despite the proliferation of software ecosystems (SECOs), growing a sustainable and healthy SECO remains a significant challenge. One approach to mitigate this challenge is the utilization of a mechanism that collects feedback from distributors (distros) and end-users of the SECO releases. This presentation aims at investigating the effectiveness of the feedback mechanism implemented by OpenStack to address the needs of end-users and distros. I mined the OpenStack repositories and mapped 20 distros' bug-related activities. Results suggest that OpenStack releases are actively maintained for 18 months before reaching end-of-life (EOL), which makes coordination with distros difficult because distros usually provide services to their end-users for a period between 36 - 60 months before reaching EOL. Also, bugs are fixed faster by the distros (7 - 76 days) than the OpenStack community (average of 4 months). However, only 22% of the bugs addressed by OpenStack distros are pushed back upstream.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719186","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}
Advent of microservices has increased the popularity of the API-first design principles. Developers have been focusing on concretising the API to a system before building the system. An API-first approach assumes that the API will be correctly used. Inevitably, most developers, even experienced ones, end-up writing sub-optimal software because of using APIs incorrectly. In this paper, we discuss an automated approach for exploring API equivalence and a framework to synthesise semantically equivalent programs. Unlike existing approaches to API transplantation, we propose an amorphous or formless approach to software translation in which a single API could potentially be replaced by a synthesised sequence of APIs which ensures type progress. Our search is guided by the non-functional goals for the software, a type-theoretic notion of progress, the application's test suite and an automatic multi-modal embedding of the API from its documentation and code analysis.
{"title":"Checkers","authors":"Santanu Kumar Dash, Fan Wu, Michail Basios, Lingbo Li, Leslie Kanthan","doi":"10.1145/3387940.3392173","DOIUrl":"https://doi.org/10.1145/3387940.3392173","url":null,"abstract":"Advent of microservices has increased the popularity of the API-first design principles. Developers have been focusing on concretising the API to a system before building the system. An API-first approach assumes that the API will be correctly used. Inevitably, most developers, even experienced ones, end-up writing sub-optimal software because of using APIs incorrectly. In this paper, we discuss an automated approach for exploring API equivalence and a framework to synthesise semantically equivalent programs. Unlike existing approaches to API transplantation, we propose an amorphous or formless approach to software translation in which a single API could potentially be replaced by a synthesised sequence of APIs which ensures type progress. Our search is guided by the non-functional goals for the software, a type-theoretic notion of progress, the application's test suite and an automatic multi-modal embedding of the API from its documentation and code analysis.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116932878","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}
Jürgen Musil, Angelika Musil, Danny Weyns, S. Biffl
The technology-driven transformation process continues to spawn novel, growth-oriented digital application domains and platforms. The user base of these society-level software systems consists of a larger proportion of the community and that involve a large set of stakeholder groups. In case of an incident there is a public demand from a variety of stakeholders for multilateral intervention in order to correct the behavior of the software system. For software engineering as a technical discipline that has been fostered and matured in corporate and organizational context, this is a major challenge because it has to deal with a multitude of multidisciplinary stakeholders and their concerns. In order to stimulate further discussions, we discuss software governance on societal level and identify future research challenges of this increasingly relevant topic.
{"title":"Society-Level Software Governance: A Challenging Scenario","authors":"Jürgen Musil, Angelika Musil, Danny Weyns, S. Biffl","doi":"10.1145/3387940.3392269","DOIUrl":"https://doi.org/10.1145/3387940.3392269","url":null,"abstract":"The technology-driven transformation process continues to spawn novel, growth-oriented digital application domains and platforms. The user base of these society-level software systems consists of a larger proportion of the community and that involve a large set of stakeholder groups. In case of an incident there is a public demand from a variety of stakeholders for multilateral intervention in order to correct the behavior of the software system. For software engineering as a technical discipline that has been fostered and matured in corporate and organizational context, this is a major challenge because it has to deal with a multitude of multidisciplinary stakeholders and their concerns. In order to stimulate further discussions, we discuss software governance on societal level and identify future research challenges of this increasingly relevant topic.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125158003","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}
Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits.
{"title":"Stack-Based Genetic Improvement","authors":"Aymeric Blot, J. Petke","doi":"10.1145/3387940.3392174","DOIUrl":"https://doi.org/10.1145/3387940.3392174","url":null,"abstract":"Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122806462","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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