The Repertory Grid Technique (RGT) has been applied within the software engineering domain to investigate a variety of topics. These include topics relating to architectural knowledge, team level tacit knowledge, and project success mechanisms. The technique is based on Personal Construct Theory (PCT) and is claimed to be suitable for gaining a deep understanding of peoples’ perspectives on a topic. The essence of RGT is a consideration of similarities and differences, for example, between different project instances. In this paper, we describe a case study in which we applied the technique with the aim of eliciting practitioners’ viewpoints on contextual factors for situated software practices. We interviewed twelve practitioners in three organisations. We found that the RGT approach was challenging to implement for several reasons. Participants had difficulty in choosing specific instances of a software practice, identifying similarities and differences tended to be problematic and causal pathways were not always easy to establish. Our contributions are the highlighting of the challenges that may occur when implementing this technique, an analysis of the issues encountered and some possible mitigation approaches. These may serve as support for SE researchers considering an RGT based study.
{"title":"Challenges when Applying Repertory Grid Technique for Software Practices","authors":"Diana Kirk, Kelly Blincoe","doi":"10.1145/3463274.3463841","DOIUrl":"https://doi.org/10.1145/3463274.3463841","url":null,"abstract":"The Repertory Grid Technique (RGT) has been applied within the software engineering domain to investigate a variety of topics. These include topics relating to architectural knowledge, team level tacit knowledge, and project success mechanisms. The technique is based on Personal Construct Theory (PCT) and is claimed to be suitable for gaining a deep understanding of peoples’ perspectives on a topic. The essence of RGT is a consideration of similarities and differences, for example, between different project instances. In this paper, we describe a case study in which we applied the technique with the aim of eliciting practitioners’ viewpoints on contextual factors for situated software practices. We interviewed twelve practitioners in three organisations. We found that the RGT approach was challenging to implement for several reasons. Participants had difficulty in choosing specific instances of a software practice, identifying similarities and differences tended to be problematic and causal pathways were not always easy to establish. Our contributions are the highlighting of the challenges that may occur when implementing this technique, an analysis of the issues encountered and some possible mitigation approaches. These may serve as support for SE researchers considering an RGT based study.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127612766","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}
Daniele Wolfart, W. K. Assunção, I. F. D. Silva, Diogo C. P. Domingos, Ederson Schmeing, Guilherme Villaca, D. Paza
Legacy systems are long-lived applications, with obsolete technology and degraded architecture. These systems hamper digital transformation and innovation, and require a great amount of resources for maintenance. The modernization of monolithic legacy systems is a strategy to promote better evolution and maintenance, taking advantage of new technologies such as microservices. Microservice architectural style is a paradigm to develop systems as a suite of small and autonomous services, communicating through a lightweight protocol. However, the migration of legacy systems to microservices is complex. Although we can find several studies on this topic, they usually focus on specific activities, e.g., the identification of the microservice boundaries in the legacy code. Also, existing pieces of work do not cover real-world scenarios, since they do not take into account organizational, operational, and technical aspects. To overcome this limitation, in this paper we present a roadmap for modernizing monolithic legacy systems with microservices. The roadmap is distilled from the existing body of knowledge, describing common activities and input/output information. The proposed roadmap is composed of eight activities, grouped in four phases, namely initiation, planning, execution, and monitoring. The main contributions are: (i) serve as a basis for practitioners to plan, execute, and monitor the modernization process; (ii) be a reference for researchers to design new studies; and (iii) motivate tool builders to deal with existing needs.
{"title":"Modernizing Legacy Systems with Microservices: A Roadmap","authors":"Daniele Wolfart, W. K. Assunção, I. F. D. Silva, Diogo C. P. Domingos, Ederson Schmeing, Guilherme Villaca, D. Paza","doi":"10.1145/3463274.3463334","DOIUrl":"https://doi.org/10.1145/3463274.3463334","url":null,"abstract":"Legacy systems are long-lived applications, with obsolete technology and degraded architecture. These systems hamper digital transformation and innovation, and require a great amount of resources for maintenance. The modernization of monolithic legacy systems is a strategy to promote better evolution and maintenance, taking advantage of new technologies such as microservices. Microservice architectural style is a paradigm to develop systems as a suite of small and autonomous services, communicating through a lightweight protocol. However, the migration of legacy systems to microservices is complex. Although we can find several studies on this topic, they usually focus on specific activities, e.g., the identification of the microservice boundaries in the legacy code. Also, existing pieces of work do not cover real-world scenarios, since they do not take into account organizational, operational, and technical aspects. To overcome this limitation, in this paper we present a roadmap for modernizing monolithic legacy systems with microservices. The roadmap is distilled from the existing body of knowledge, describing common activities and input/output information. The proposed roadmap is composed of eight activities, grouped in four phases, namely initiation, planning, execution, and monitoring. The main contributions are: (i) serve as a basis for practitioners to plan, execute, and monitor the modernization process; (ii) be a reference for researchers to design new studies; and (iii) motivate tool builders to deal with existing needs.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131524728","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}
The rapid growth of software engineering research publications forces an amount of scholarly knowledge that needs to be managed, organized and communicated in digital libraries and scientific search engines. Thus, there is a need for classified papers to accomplish these tasks, but the classification process is cumbersome. Moreover, in case of new schemas, one would need to reclassify previously published research. We propose to automate the classification and present different possible techniques for doing so: Using natural language models, a rule-based approach, or an approach based on topic-labeling. In this proposal paper, we initially implemented a prototype for text classification of software engineering research papers.
{"title":"Towards an Automated Classification Approach for Software Engineering Research","authors":"Angelika Kaplan, Jan Keim","doi":"10.1145/3463274.3463358","DOIUrl":"https://doi.org/10.1145/3463274.3463358","url":null,"abstract":"The rapid growth of software engineering research publications forces an amount of scholarly knowledge that needs to be managed, organized and communicated in digital libraries and scientific search engines. Thus, there is a need for classified papers to accomplish these tasks, but the classification process is cumbersome. Moreover, in case of new schemas, one would need to reclassify previously published research. We propose to automate the classification and present different possible techniques for doing so: Using natural language models, a rule-based approach, or an approach based on topic-labeling. In this proposal paper, we initially implemented a prototype for text classification of software engineering research papers.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114838237","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}
Context: The Google Play Store allows app developers to respond to user reviews. Existing research shows that response strategies vary considerably. In addition, while responding to reviews can lead to several types of favorable outcomes, not every response leads to success, which we define as increased user ratings. Aims: This work has two objectives. The first is to investigate the potential to predict early whether a developer response to a review is likely to be successful. The second is to pinpoint how developers can increase the chance of their responses to achieve success. Method: We track changes in user reviews of the 1,600 top free apps over a ten-week period, and find that in 11,034 out of 228,274 one- to four-star reviews, the ratings increase after a response. We extract three groups of features, namely time, presentation and tone, from the responses given to these reviews. We apply the extreme gradient boosting (XGBoost) algorithm to model the success of developer responses using these features. We employ model interpretation techniques to derive insights from the model. Results: Our model can achieve an AUC of 0.69, thus demonstrating that feature engineering and machine learning have the potential to enable developers to estimate the probability of success of their responses at composition time. We learn from it that the ratio between the length of the review and response, the textual similarity between the review and response, and the timeliness and the politeness of the response have the highest predictive power for distinguishing successful and unsuccessful developer responses. Conclusions: Based on our findings, we provide recommendations that developers can follow to increase the chance of success of their responses. Tools may also leverage our findings to support developers in writing more effective responses to reviews on the app store.
背景:Google Play Store允许应用开发者对用户评论做出回应。现有的研究表明，应对策略差异很大。此外，虽然回复评论可以带来几种有利的结果，但并不是每个回复都会带来成功，我们将其定义为增加用户评分。目的:这项工作有两个目标。第一个是调查早期预测开发人员对评审的响应是否可能成功的潜力。第二点是确定开发人员如何增加他们的响应获得成功的机会。方法:我们在10周内追踪了1600款最受欢迎的免费应用的用户评论变化，发现在228,274条一星到四星的评论中，有11034条在用户回复后评级上升。我们从对这些评论的回应中提取出三组特征，即时间、呈现和语气。我们应用极端梯度增强(XGBoost)算法来模拟使用这些特征的开发人员响应的成功。我们采用模型解释技术从模型中获得见解。结果:我们的模型可以达到0.69的AUC，从而表明特征工程和机器学习有潜力使开发人员能够在组合时估计他们的响应成功的概率。我们从中得知，评论和回复的长度之比、评论和回复的文本相似性、回复的及时性和礼貌性对区分成功和不成功的开发者回复具有最高的预测能力。结论:基于我们的发现，我们提供了开发者可以遵循的建议，以增加他们的回应成功的机会。工具还可以利用我们的发现，帮助开发者更有效地回应应用商店的评论。
{"title":"How Should Developers Respond to App Reviews? Features Predicting the Success of Developer Responses","authors":"Kamonphop Srisopha, Daniel Link, Barry W. Boehm","doi":"10.1145/3463274.3463311","DOIUrl":"https://doi.org/10.1145/3463274.3463311","url":null,"abstract":"Context: The Google Play Store allows app developers to respond to user reviews. Existing research shows that response strategies vary considerably. In addition, while responding to reviews can lead to several types of favorable outcomes, not every response leads to success, which we define as increased user ratings. Aims: This work has two objectives. The first is to investigate the potential to predict early whether a developer response to a review is likely to be successful. The second is to pinpoint how developers can increase the chance of their responses to achieve success. Method: We track changes in user reviews of the 1,600 top free apps over a ten-week period, and find that in 11,034 out of 228,274 one- to four-star reviews, the ratings increase after a response. We extract three groups of features, namely time, presentation and tone, from the responses given to these reviews. We apply the extreme gradient boosting (XGBoost) algorithm to model the success of developer responses using these features. We employ model interpretation techniques to derive insights from the model. Results: Our model can achieve an AUC of 0.69, thus demonstrating that feature engineering and machine learning have the potential to enable developers to estimate the probability of success of their responses at composition time. We learn from it that the ratio between the length of the review and response, the textual similarity between the review and response, and the timeliness and the politeness of the response have the highest predictive power for distinguishing successful and unsuccessful developer responses. Conclusions: Based on our findings, we provide recommendations that developers can follow to increase the chance of success of their responses. Tools may also leverage our findings to support developers in writing more effective responses to reviews on the app store.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475432","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}
Phuong T. Nguyen, Davide Di Ruscio, Juri Di Rocco, Claudio Di Sipio, M. Di Penta
In recent years, we have witnessed a dramatic increase in the application of Machine Learning algorithms in several domains, including the development of recommender systems for software engineering (RSSE). While researchers focused on the underpinning ML techniques to improve recommendation accuracy, little attention has been paid to make such systems robust and resilient to malicious data. By manipulating the algorithms’ training set, i.e., large open-source software (OSS) repositories, it would be possible to make recommender systems vulnerable to adversarial attacks. This paper presents an initial investigation of adversarial machine learning and its possible implications on RSSE. As a proof-of-concept, we show the extent to which the presence of manipulated data can have a negative impact on the outcomes of two state-of-the-art recommender systems which suggest third-party libraries to developers. Our work aims at raising awareness of adversarial techniques and their effects on the Software Engineering community. We also propose equipping recommender systems with the capability to learn to dodge adversarial activities.
{"title":"Adversarial Machine Learning: On the Resilience of Third-party Library Recommender Systems","authors":"Phuong T. Nguyen, Davide Di Ruscio, Juri Di Rocco, Claudio Di Sipio, M. Di Penta","doi":"10.1145/3463274.3463809","DOIUrl":"https://doi.org/10.1145/3463274.3463809","url":null,"abstract":"In recent years, we have witnessed a dramatic increase in the application of Machine Learning algorithms in several domains, including the development of recommender systems for software engineering (RSSE). While researchers focused on the underpinning ML techniques to improve recommendation accuracy, little attention has been paid to make such systems robust and resilient to malicious data. By manipulating the algorithms’ training set, i.e., large open-source software (OSS) repositories, it would be possible to make recommender systems vulnerable to adversarial attacks. This paper presents an initial investigation of adversarial machine learning and its possible implications on RSSE. As a proof-of-concept, we show the extent to which the presence of manipulated data can have a negative impact on the outcomes of two state-of-the-art recommender systems which suggest third-party libraries to developers. Our work aims at raising awareness of adversarial techniques and their effects on the Software Engineering community. We also propose equipping recommender systems with the capability to learn to dodge adversarial activities.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331735","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}
With its unique advantages such as decentralization and immutability, blockchain technology has been widely used in various fields in recent years. The smart contract running on the blockchain is also playing an increasingly important role in decentralized application scenarios. Therefore, the automatic detection of security vulnerabilities in smart contracts has become an urgent problem in the application of blockchain technology. Hyperledger Fabric is a smart contract platform based on enterprise-level licensed distributed ledger technology. However, the research on the vulnerability detection technology of Hyperledger Fabric smart contracts is still in its infancy. In this paper, we propose HFContractFuzzer, a method based on Fuzzing technology to detect Hyperledger Fabric smart contracts, which combines a Fuzzing tool for golang named go-fuzz and smart contracts written by golang. We use HFContractFuzzer to detect vulnerabilities in five contracts from typical sources and discover that four of them have security vulnerabilities, proving the effectiveness of the proposed method.
{"title":"HFContractFuzzer: Fuzzing Hyperledger Fabric Smart Contracts for Vulnerability Detection","authors":"Mengjie Ding, Peiru Li, Shanshan Li, He Zhang","doi":"10.1145/3463274.3463351","DOIUrl":"https://doi.org/10.1145/3463274.3463351","url":null,"abstract":"With its unique advantages such as decentralization and immutability, blockchain technology has been widely used in various fields in recent years. The smart contract running on the blockchain is also playing an increasingly important role in decentralized application scenarios. Therefore, the automatic detection of security vulnerabilities in smart contracts has become an urgent problem in the application of blockchain technology. Hyperledger Fabric is a smart contract platform based on enterprise-level licensed distributed ledger technology. However, the research on the vulnerability detection technology of Hyperledger Fabric smart contracts is still in its infancy. In this paper, we propose HFContractFuzzer, a method based on Fuzzing technology to detect Hyperledger Fabric smart contracts, which combines a Fuzzing tool for golang named go-fuzz and smart contracts written by golang. We use HFContractFuzzer to detect vulnerabilities in five contracts from typical sources and discover that four of them have security vulnerabilities, proving the effectiveness of the proposed method.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"53 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127235821","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}
In large, collaborative open-source projects, developers must follow good coding standards to ensure the quality and sustainability of the resulting software. This is especially a challenge in high-performance computing projects, which admit a diverse set of contributions over decades of development. Some successful projects, such as the Portable, Extensible Toolkit for Scientific Computation (PETSc), have created comprehensive developer documentation, including specific code quality rules, which should be followed by contributors. However, none of the widely used and highly active open-source HPC projects have a way to automatically check whether these rules, typically expressed informally in English, are being violated. Hence, compliance checking is labor-intensive and difficult to ensure. To address this issue, we propose an automated method for detecting rule violations in HPC applications based on the PETSc development rules. In our empirical study, we consider 46 PETSc-based applications and assess the violations of two C-usage rules. The experimental results demonstrate the efficacy of the proposed method in identifying PETSc rule violations, which can be broadened to other HPC frameworks and extended by us and others in the community to include more rules.
{"title":"Empirical Investigation of Code Quality Rule Violations in HPC Applications","authors":"Shahid Hussain, Kaley M Chicoine, B. Norris","doi":"10.1145/3463274.3463787","DOIUrl":"https://doi.org/10.1145/3463274.3463787","url":null,"abstract":"In large, collaborative open-source projects, developers must follow good coding standards to ensure the quality and sustainability of the resulting software. This is especially a challenge in high-performance computing projects, which admit a diverse set of contributions over decades of development. Some successful projects, such as the Portable, Extensible Toolkit for Scientific Computation (PETSc), have created comprehensive developer documentation, including specific code quality rules, which should be followed by contributors. However, none of the widely used and highly active open-source HPC projects have a way to automatically check whether these rules, typically expressed informally in English, are being violated. Hence, compliance checking is labor-intensive and difficult to ensure. To address this issue, we propose an automated method for detecting rule violations in HPC applications based on the PETSc development rules. In our empirical study, we consider 46 PETSc-based applications and assess the violations of two C-usage rules. The experimental results demonstrate the efficacy of the proposed method in identifying PETSc rule violations, which can be broadened to other HPC frameworks and extended by us and others in the community to include more rules.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131276920","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}
Scientific progress comes from creating sound theories. However, current software engineering still mostly falls short of this goal, although its importance is widely accepted. Thus, in this paper, we discuss the importance of a successful interaction of empirical research with a strong theoretical basis and the ramifications this has. In particular, we will extensively discuss the implications on theory building and the empirical vs. theory interaction, etc. While not everything we will discuss is novel, we present a number of insights, which we at least did not see in software engineering literature. We strongly believe that a careful consideration of the insights discussed in this paper has the potential to lead to a significant improvement in software engineering research.
{"title":"If you want better empirical research, value your theory: On the importance of strong theories for progress in empirical software engineering research","authors":"Klaus Schmid","doi":"10.1145/3463274.3463360","DOIUrl":"https://doi.org/10.1145/3463274.3463360","url":null,"abstract":"Scientific progress comes from creating sound theories. However, current software engineering still mostly falls short of this goal, although its importance is widely accepted. Thus, in this paper, we discuss the importance of a successful interaction of empirical research with a strong theoretical basis and the ramifications this has. In particular, we will extensively discuss the implications on theory building and the empirical vs. theory interaction, etc. While not everything we will discuss is novel, we present a number of insights, which we at least did not see in software engineering literature. We strongly believe that a careful consideration of the insights discussed in this paper has the potential to lead to a significant improvement in software engineering research.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"12 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120806934","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}
This taxonomy paper presents a novel way of knowledge engineering in the software engineering research community. Till now, research papers are organized digitally as documents, mostly in PDF files. Not much effort is spent on effective knowledge classification, retrieval, storage, and representation. In contrast to the current paper-based approach for knowledge documentation, we present a statement-based approach, where each statement is linked to arguments and data of its evidence as well as to related statements. We argue that in this way, knowledge will be easier to retrieve, compare, and evaluate in contrast to current paper-based knowledge engineering in scientific search engines and digital libraries. Therefore, we present as a first step a novel multi-dimensional classification for statements in software engineering research. Statements are classified according to their research object, their kind (e.g., relevance), and their underlying evidence. This classification is validated and extended with a first systematic literature review. Additionally, we provide an example for illustration purpose.
{"title":"A Classification for Managing Software Engineering Knowledge","authors":"Angelika Kaplan, Maximilian Walter, R. Heinrich","doi":"10.1145/3463274.3463357","DOIUrl":"https://doi.org/10.1145/3463274.3463357","url":null,"abstract":"This taxonomy paper presents a novel way of knowledge engineering in the software engineering research community. Till now, research papers are organized digitally as documents, mostly in PDF files. Not much effort is spent on effective knowledge classification, retrieval, storage, and representation. In contrast to the current paper-based approach for knowledge documentation, we present a statement-based approach, where each statement is linked to arguments and data of its evidence as well as to related statements. We argue that in this way, knowledge will be easier to retrieve, compare, and evaluate in contrast to current paper-based knowledge engineering in scientific search engines and digital libraries. Therefore, we present as a first step a novel multi-dimensional classification for statements in software engineering research. Statements are classified according to their research object, their kind (e.g., relevance), and their underlying evidence. This classification is validated and extended with a first systematic literature review. Additionally, we provide an example for illustration purpose.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840259","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}
Usman Rafiq, Jorge Melegati, Dron Khanna, E. Guerra, Xiaofeng Wang
[Context] Software startups are engines of innovation and economy, yet building software startups is challenging and subject to a high failure rate. They need to act and respond fast in highly uncertain business environments. To do so, they need to identify crucial and actionable information that supports them in making correct decisions and reduce uncertainty. So far, the software startup literature focused predominantly on what information to measure from a metrics perspective. Thus, there is a lack of research investigating how to deal with information from an analytics perspective. [Objective] The current study aims at understanding how software startups are dealing with crucial information that could lead to meaningful actions. The overall research question that guides the study is: what analytics mistakes do software startups make? [Method] We investigated 22 failed software startups using their post-mortem reports as the main source. They were included in the study because the founding teams made mistakes related to information and analytics, which contributed to their startup failure to various degrees. We analyzed the collected data using thematic analysis. [Results] Ten types of mistakes made by the 22 failed startups when dealing with information are identified. These ten types are further grouped into four categories from an analytics process perspective, including information collection, information analysis, information communication, and information usage. [Conclusions] Our findings contribute to a better understanding of how software startups are dealing with information. It provides an opportunity for software startup teams to learn from the recurring mistakes of failed startups. Interesting future research avenues include defining patterns and antipatterns in software startup analytics by studying both failed and successful startups and doing an in-depth investigation of essential metrics for software startups.
{"title":"Analytics Mistakes that Derail Software Startups","authors":"Usman Rafiq, Jorge Melegati, Dron Khanna, E. Guerra, Xiaofeng Wang","doi":"10.1145/3463274.3463305","DOIUrl":"https://doi.org/10.1145/3463274.3463305","url":null,"abstract":"[Context] Software startups are engines of innovation and economy, yet building software startups is challenging and subject to a high failure rate. They need to act and respond fast in highly uncertain business environments. To do so, they need to identify crucial and actionable information that supports them in making correct decisions and reduce uncertainty. So far, the software startup literature focused predominantly on what information to measure from a metrics perspective. Thus, there is a lack of research investigating how to deal with information from an analytics perspective. [Objective] The current study aims at understanding how software startups are dealing with crucial information that could lead to meaningful actions. The overall research question that guides the study is: what analytics mistakes do software startups make? [Method] We investigated 22 failed software startups using their post-mortem reports as the main source. They were included in the study because the founding teams made mistakes related to information and analytics, which contributed to their startup failure to various degrees. We analyzed the collected data using thematic analysis. [Results] Ten types of mistakes made by the 22 failed startups when dealing with information are identified. These ten types are further grouped into four categories from an analytics process perspective, including information collection, information analysis, information communication, and information usage. [Conclusions] Our findings contribute to a better understanding of how software startups are dealing with information. It provides an opportunity for software startup teams to learn from the recurring mistakes of failed startups. Interesting future research avenues include defining patterns and antipatterns in software startup analytics by studying both failed and successful startups and doing an in-depth investigation of essential metrics for software startups.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970161","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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