F. Ferrucci, C. Gravino, R. Oliveto, Federica Sarro
Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.
{"title":"Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions","authors":"F. Ferrucci, C. Gravino, R. Oliveto, Federica Sarro","doi":"10.1109/SSBSE.2010.20","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.20","url":null,"abstract":"Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241030","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. Shevertalov, Kevin Lynch, E. Stehle, C. Rorres, S. Mancoridis
Fault-detection approaches in autonomic systems typically rely on runtime software sensors to compute metrics for CPU utilization, memory usage, network throughput, and so on. One detection approach uses data collected by the runtime sensors to construct a convex-hull geometric object whose interior represents the normal execution of the monitored application. The approach detects faults by classifying the current application state as being either inside or outside of the convex hull. However, due to the computational complexity of creating a convex hull in multi-dimensional space, the convex-hull approach is limited to a few metrics. Therefore, not all sensors can be used to detect faults and so some must be dropped or combined with others. This paper compares the effectiveness of genetic-programming, genetic-algorithm, and random-search approaches in solving the problem of selecting sensors and combining them into metrics. These techniques are used to find 8 metrics that are derived from a set of 21 available sensors. The metrics are used to detect faults during the execution of a Java-based HTTP web server. The results of the search techniques are compared to two hand-crafted solutions specified by experts.
{"title":"Using Search Methods for Selecting and Combining Software Sensors to Improve Fault Detection in Autonomic Systems","authors":"M. Shevertalov, Kevin Lynch, E. Stehle, C. Rorres, S. Mancoridis","doi":"10.1109/SSBSE.2010.23","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.23","url":null,"abstract":"Fault-detection approaches in autonomic systems typically rely on runtime software sensors to compute metrics for CPU utilization, memory usage, network throughput, and so on. One detection approach uses data collected by the runtime sensors to construct a convex-hull geometric object whose interior represents the normal execution of the monitored application. The approach detects faults by classifying the current application state as being either inside or outside of the convex hull. However, due to the computational complexity of creating a convex hull in multi-dimensional space, the convex-hull approach is limited to a few metrics. Therefore, not all sensors can be used to detect faults and so some must be dropped or combined with others. This paper compares the effectiveness of genetic-programming, genetic-algorithm, and random-search approaches in solving the problem of selecting sensors and combining them into metrics. These techniques are used to find 8 metrics that are derived from a set of 21 available sensors. The metrics are used to detect faults during the execution of a Java-based HTTP web server. The results of the search techniques are compared to two hand-crafted solutions specified by experts.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756952","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}
A major activity performed by the manager before starting a software project is tailoring its development process. Such activity requires information about the context under which the project will be executed, including organizational, project, and team characteristics. In addition, it also requires pondering many factors and evaluating all existing constraints. In this scenario, we claim that a balance between collaboration and discipline can be the drivers to tailor software development processes in order to meet project and organization needs. With the purpose of facilitating this balancing, it is possible to automate some of the steps to solve the problem, reducing the effort required to execute this task and improving the obtained process. Therefore, this work presents an optimization-based approach where the balancing in process tailoring is defined, modeled and briefly analyzed. This approach uses collaboration and discipline as utility functions to select the most appropriate process for a software development project, considering its current context.
{"title":"An Optimization-based Approach to Software Development Process Tailoring","authors":"A. Magdaleno","doi":"10.1109/SSBSE.2010.15","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.15","url":null,"abstract":"A major activity performed by the manager before starting a software project is tailoring its development process. Such activity requires information about the context under which the project will be executed, including organizational, project, and team characteristics. In addition, it also requires pondering many factors and evaluating all existing constraints. In this scenario, we claim that a balance between collaboration and discipline can be the drivers to tailor software development processes in order to meet project and organization needs. With the purpose of facilitating this balancing, it is possible to automate some of the steps to solve the problem, reducing the effort required to execute this task and improving the obtained process. Therefore, this work presents an optimization-based approach where the balancing in process tailoring is defined, modeled and briefly analyzed. This approach uses collaboration and discipline as utility functions to select the most appropriate process for a software development project, considering its current context.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132277554","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 software testing phase usually results in a large number of bugs to be fixed. The fixing of these bugs require executing certain activities (potentially concurrent) that demand resources having different competencies and workloads. Appropriate resource allocation to these bug-fixing activities can help a project manager to schedule capable resources to these activities, taking into account their availability and skill requirements for fixing different bugs. This paper presents a multi-objective search-based resource scheduling method for bug-fixing tasks. The inputs to our proposed method include i) a bug model, ii) a human resource model, iii) a capability matching method between bug-fixing activities and human resources and iv) objectives of bug-fixing. A genetic algorithm (GA) is used as a search algorithm and the output is a bug-fixing schedule, satisfying different constraints and value objectives. We have evaluated our proposed scheduling method on an industrial data set and have discussed three different scenarios. The results indicate that GA is able to effectively schedule resources by balancing different objectives. We have also compared the effectiveness of using GA with a simple hill climbing algorithm. The comparison shows that GA is able to achieve statistically better fitness values than hill-climbing.
{"title":"Search-based Resource Scheduling for Bug Fixing Tasks","authors":"Junchao Xiao, W. Afzal","doi":"10.1109/SSBSE.2010.24","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.24","url":null,"abstract":"The software testing phase usually results in a large number of bugs to be fixed. The fixing of these bugs require executing certain activities (potentially concurrent) that demand resources having different competencies and workloads. Appropriate resource allocation to these bug-fixing activities can help a project manager to schedule capable resources to these activities, taking into account their availability and skill requirements for fixing different bugs. This paper presents a multi-objective search-based resource scheduling method for bug-fixing tasks. The inputs to our proposed method include i) a bug model, ii) a human resource model, iii) a capability matching method between bug-fixing activities and human resources and iv) objectives of bug-fixing. A genetic algorithm (GA) is used as a search algorithm and the output is a bug-fixing schedule, satisfying different constraints and value objectives. We have evaluated our proposed scheduling method on an industrial data set and have discussed three different scenarios. The results indicate that GA is able to effectively schedule resources by balancing different objectives. We have also compared the effectiveness of using GA with a simple hill climbing algorithm. The comparison shows that GA is able to achieve statistically better fitness values than hill-climbing.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557540","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 selection of the enhancements to be included in the next software release is a complex task in every software development. Customers demand their own software enhancements, but all of them cannot be included in the software product, mainly due to the existence limited resources. In most of the cases, it is not feasible to develop all the new functionalities suggested by customers. Hence each new feature competes against each other to be included in the next release. This problem of minimizing development effort and maximizing customers’ satisfaction is known as the next release problem (NRP). In this work we study the NRP problem as an optimisation problem. We use and describe three different meta-heuristic search techniques for solving NRP: simulated annealing, genetic algorithms and ant colony system (specifically, we show how to adapt the ant colony system to NRP). All of them obtain good but possibly sub optimal solution. Also we make a comparative study of these techniques on a case study. Furthermore, we have observed that the sub optimal solutions found applying these techniques include a high percentage of the requirements considered as most important by each individual customer.
{"title":"Ant Colony Optimization for the Next Release Problem: A Comparative Study","authors":"J. del Sagrado, I. M. Del Águila, F. J. Orellana","doi":"10.1109/SSBSE.2010.18","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.18","url":null,"abstract":"The selection of the enhancements to be included in the next software release is a complex task in every software development. Customers demand their own software enhancements, but all of them cannot be included in the software product, mainly due to the existence limited resources. In most of the cases, it is not feasible to develop all the new functionalities suggested by customers. Hence each new feature competes against each other to be included in the next release. This problem of minimizing development effort and maximizing customers’ satisfaction is known as the next release problem (NRP). In this work we study the NRP problem as an optimisation problem. We use and describe three different meta-heuristic search techniques for solving NRP: simulated annealing, genetic algorithms and ant colony system (specifically, we show how to adapt the ant colony system to NRP). All of them obtain good but possibly sub optimal solution. Also we make a comparative study of these techniques on a case study. Furthermore, we have observed that the sub optimal solutions found applying these techniques include a high percentage of the requirements considered as most important by each individual customer.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115265279","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}
There has been much recent interest in Search Based Optimization for Requirements Selection from the SBSE community, demonstrating how multi-objective techniques can effectively balance the competing cost and value objectives inherent in requirements selection. This problem is known as release planning (aka the ‘next release problem). However, little previous work has considered the problem of Requirement Interaction Management (RIM) in the solution space. Because of RIM, there are many subtle relationships between requirements, which make the problem more complex than an unconstrained feature subset selection problem. This paper introduces and evaluates archive-based multi-objective evolutionary algorithm, based on NSGA-II, which is capable of maintaining solution quality and diversity, while respecting the constraints imposed by RIM.
{"title":"Search Based Optimization of Requirements Interaction Management","authors":"Yuanyuan Zhang, M. Harman","doi":"10.1109/SSBSE.2010.16","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.16","url":null,"abstract":"There has been much recent interest in Search Based Optimization for Requirements Selection from the SBSE community, demonstrating how multi-objective techniques can effectively balance the competing cost and value objectives inherent in requirements selection. This problem is known as release planning (aka the ‘next release problem). However, little previous work has considered the problem of Requirement Interaction Management (RIM) in the solution space. Because of RIM, there are many subtle relationships between requirements, which make the problem more complex than an unconstrained feature subset selection problem. This paper introduces and evaluates archive-based multi-objective evolutionary algorithm, based on NSGA-II, which is capable of maintaining solution quality and diversity, while respecting the constraints imposed by RIM.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128166081","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}
Search-based techniques were successfully applied to many different areas of testing but according to our knowledge there are no works that applies search-based techniques to testing of concurrent software, yet. This PhD paper describes plans and already achieved preliminary results with applying search-based techniques to testing of concurrent software. In particular, we plan to combine noise injection techniques for testing of concurrent software, various concurrency coverage measures, and several dynamic analyses with search-based optimization techniques.
{"title":"Sophisticated Testing of Concurrent Programs","authors":"Zdenek Letko","doi":"10.1109/SSBSE.2010.14","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.14","url":null,"abstract":"Search-based techniques were successfully applied to many different areas of testing but according to our knowledge there are no works that applies search-based techniques to testing of concurrent software, yet. This PhD paper describes plans and already achieved preliminary results with applying search-based techniques to testing of concurrent software. In particular, we plan to combine noise injection techniques for testing of concurrent software, various concurrency coverage measures, and several dynamic analyses with search-based optimization techniques.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123727181","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 potential of applying search-based testing principles to functional testing has been demonstrated in various cases. The focus was mainly on simulating the system under test using a model or compiled source code in order to evaluate test cases. However, in many cases only the final hardware unit is available for testing. This research presents an approach in which evolutionary functional testing is performed using an actual electronic control unit for test case evaluation. A test environment designed to be used for large-scale industrial systems is introduced. An extensive case study has been carried out to assess its capabilities. Results indicate that the approach proposed in this work is suitable for automated functional testing of embedded control systems within a Hardware-in the-Loop test environment.
{"title":"A Search-Based Approach to Functional Hardware-in-the-Loop Testing","authors":"Felix F. Lindlar, Andreas Windisch","doi":"10.1109/SSBSE.2010.22","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.22","url":null,"abstract":"The potential of applying search-based testing principles to functional testing has been demonstrated in various cases. The focus was mainly on simulating the system under test using a model or compiled source code in order to evaluate test cases. However, in many cases only the final hardware unit is available for testing. This research presents an approach in which evolutionary functional testing is performed using an actual electronic control unit for test case evaluation. A test environment designed to be used for large-scale industrial systems is introduced. An extensive case study has been carried out to assess its capabilities. Results indicate that the approach proposed in this work is suitable for automated functional testing of embedded control systems within a Hardware-in the-Loop test environment.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165104","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 algorithms are attractive to solve many search-based software engineering problems because they allow the easy parallelization of computations, which improves scalability and reduces computation time. In this paper, we present our experience in applying different distributed architectures to parallelize a genetic algorithm used to solve the concept identification problem. We developed an approach to identify concepts in execution traces by finding cohesive and decoupled fragments of the traces. The approach relies on a genetic algorithm, on a textual analysis of source code using latent semantic indexing, and on trace compression techniques. The fitness function in our approach has a polynomial evaluation cost and is highly computationally intensive. A run of our approach on a trace of thousand methods may require several hours of computation on a standard PC. Consequently, we reduced computation time by parallelizing the genetic algorithm at the core of our approach over a standard TCP/IP network. We developed four distributed architectures and compared their performances: we observed a decrease of computation time up to 140 times. Although presented in the context of concept location, our findings could be applied to many other search-based software engineering problems.
{"title":"Concept Location with Genetic Algorithms: A Comparison of Four Distributed Architectures","authors":"F. Asadi, G. Antoniol, Yann-Gaël Guéhéneuc","doi":"10.1109/SSBSE.2010.26","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.26","url":null,"abstract":"Genetic algorithms are attractive to solve many search-based software engineering problems because they allow the easy parallelization of computations, which improves scalability and reduces computation time. In this paper, we present our experience in applying different distributed architectures to parallelize a genetic algorithm used to solve the concept identification problem. We developed an approach to identify concepts in execution traces by finding cohesive and decoupled fragments of the traces. The approach relies on a genetic algorithm, on a textual analysis of source code using latent semantic indexing, and on trace compression techniques. The fitness function in our approach has a polynomial evaluation cost and is highly computationally intensive. A run of our approach on a trace of thousand methods may require several hours of computation on a standard PC. Consequently, we reduced computation time by parallelizing the genetic algorithm at the core of our approach over a standard TCP/IP network. We developed four distributed architectures and compared their performances: we observed a decrease of computation time up to 140 times. Although presented in the context of concept location, our findings could be applied to many other search-based software engineering problems.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"5 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791966","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}
Recent research in search-based software engineering (SBSE) has demonstrated that a number of software engineering problems can be reformulated as a search problem, hence search algorithms can be applied to tackle it. However, most of the existing work has been of empirical nature and the techniques are predominately experimental. Therefore in-depth studies into characteritics of SE problems and appropriate algorithms to solve them are necessary. In this paper, we propose a novel method to gain insight knowledge on a variant of the next release problem (NRP) using elementary landscape analysis, which could be used to guide the design of more efficient algorithms. Preliminary experimental results are obtained to indicate the effectiveness of the proposed method.
{"title":"Applying Elementary Landscape Analysis to Search-Based Software Engineering","authors":"Guanzhou Lu, R. Bahsoon, X. Yao","doi":"10.1109/SSBSE.2010.10","DOIUrl":"https://doi.org/10.1109/SSBSE.2010.10","url":null,"abstract":"Recent research in search-based software engineering (SBSE) has demonstrated that a number of software engineering problems can be reformulated as a search problem, hence search algorithms can be applied to tackle it. However, most of the existing work has been of empirical nature and the techniques are predominately experimental. Therefore in-depth studies into characteritics of SE problems and appropriate algorithms to solve them are necessary. In this paper, we propose a novel method to gain insight knowledge on a variant of the next release problem (NRP) using elementary landscape analysis, which could be used to guide the design of more efficient algorithms. Preliminary experimental results are obtained to indicate the effectiveness of the proposed method.","PeriodicalId":309806,"journal":{"name":"2nd International Symposium on Search Based Software Engineering","volume":"648 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133215531","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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