The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform

Guenther Ruhe - One of the best experts on this subject based on the ideXlab platform.

  • a two staged survey on Release Readiness
    Evaluation and Assessment in Software Engineering, 2017
    Co-Authors: S Didar Al M Alam, Dietmar Pfahl, Maleknaz Nayebi, Guenther Ruhe
    Abstract:

    Deciding about the content and Readiness when shipping a new product Release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world Release decisions and to what extent research on Release Readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make Release Readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making Release Readiness decisions. We found that reasons for failed Releases and the factors considered for making Release decisions are context specific and vary with Release cycle time. Practitioners confirmed that (i) Release Readiness should be measured and continuously monitored during the whole Release cycle, (ii) Release Readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed Releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of Release development were articulated as key requirements.

  • EASE - A Two-staged Survey on Release Readiness
    Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, 2017
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Maleknaz Nayebi, Guenther Ruhe
    Abstract:

    Deciding about the content and Readiness when shipping a new product Release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world Release decisions and to what extent research on Release Readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make Release Readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making Release Readiness decisions. We found that reasons for failed Releases and the factors considered for making Release decisions are context specific and vary with Release cycle time. Practitioners confirmed that (i) Release Readiness should be measured and continuously monitored during the whole Release cycle, (ii) Release Readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed Releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of Release development were articulated as key requirements.

  • Release Readiness classification an explorative case study
    Empirical Software Engineering and Measurement, 2016
    Co-Authors: S Didar Al M Alam, Dietmar Pfahl, Guenther Ruhe
    Abstract:

    Context: To survive in a highly competitive software market, product managers are striving for frequent, incremental Releases in ever shorter cycles. Release decisions are characterized by high complexity and have a high impact on project success. Under such conditions, using the experience from past Releases could help product managers to take more informed decisions. Goal and research objectives: To make decisions about when to make a Release more operational, we formulated Release Readiness (RR) as a binary classification problem. The goal of our research presented in this paper is twofold: (i) to propose a machine learning approach called RC* (Release Readiness Classification applying predictive techniques) with two approaches for defining the training set called incremental and sliding window, and (ii) to empirically evaluate the applicability of RC* for varying project characteristics. Methodology: In the form of explorative case study research, we applied the RC* method to four OSS projects under the Apache Software Foundation. We retrospectively covered a period of 82 months, 90 Releases and 3722 issues. We use Random Forest as the classification technique along with eight independent variables to classify Release Readiness in individual weeks. Predictive performance was measured in terms of precision, recall, F-measure, and accuracy. Results: The incremental and sliding window approaches respectively achieve an overall 76% and 79% accuracy in classifying RR for four analyzed projects. Incremental approach outperforms sliding window approach in terms of stability of the predictive performance. Predictive performance for both approaches are significantly influenced by three project characteristics i) Release duration, ii) number of issues in a Release, iii) size of the initial training dataset. Conclusion: As our initial observation we identified, incremental approach achieves higher accuracy when Releases have long duration, low number of issues and classifiers are trained with large training set. On the other hand, sliding window approach achieves higher accuracy when Releases have short duration and classifiers are trained with small training set.

  • ESEM - Release Readiness Classification: An Explorative Case Study
    Proceedings of the 10th ACM IEEE International Symposium on Empirical Software Engineering and Measurement, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Guenther Ruhe
    Abstract:

    Context: To survive in a highly competitive software market, product managers are striving for frequent, incremental Releases in ever shorter cycles. Release decisions are characterized by high complexity and have a high impact on project success. Under such conditions, using the experience from past Releases could help product managers to take more informed decisions. Goal and research objectives: To make decisions about when to make a Release more operational, we formulated Release Readiness (RR) as a binary classification problem. The goal of our research presented in this paper is twofold: (i) to propose a machine learning approach called RC* (Release Readiness Classification applying predictive techniques) with two approaches for defining the training set called incremental and sliding window, and (ii) to empirically evaluate the applicability of RC* for varying project characteristics. Methodology: In the form of explorative case study research, we applied the RC* method to four OSS projects under the Apache Software Foundation. We retrospectively covered a period of 82 months, 90 Releases and 3722 issues. We use Random Forest as the classification technique along with eight independent variables to classify Release Readiness in individual weeks. Predictive performance was measured in terms of precision, recall, F-measure, and accuracy. Results: The incremental and sliding window approaches respectively achieve an overall 76% and 79% accuracy in classifying RR for four analyzed projects. Incremental approach outperforms sliding window approach in terms of stability of the predictive performance. Predictive performance for both approaches are significantly influenced by three project characteristics i) Release duration, ii) number of issues in a Release, iii) size of the initial training dataset. Conclusion: As our initial observation we identified, incremental approach achieves higher accuracy when Releases have long duration, low number of issues and classifiers are trained with large training set. On the other hand, sliding window approach achieves higher accuracy when Releases have short duration and classifiers are trained with small training set.

  • analysis and improvement of Release Readiness a genetic optimization approach
    Product Focused Software Process Improvement, 2014
    Co-Authors: S M Didaralalam, Dietmar Pfahl, Shawn Shahnewaz, Guenther Ruhe
    Abstract:

    Context: Release Readiness (RR) quantifies the status of a product Release by aggregating a portfolio of Release related measures. Early identification of factors responsible in improving RR (i.e. RR improvement factors) can help project managers to (re)allocate resources to improve processes to achieve higher level of RR score.

Dietmar Pfahl - One of the best experts on this subject based on the ideXlab platform.

  • a two staged survey on Release Readiness
    Evaluation and Assessment in Software Engineering, 2017
    Co-Authors: S Didar Al M Alam, Dietmar Pfahl, Maleknaz Nayebi, Guenther Ruhe
    Abstract:

    Deciding about the content and Readiness when shipping a new product Release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world Release decisions and to what extent research on Release Readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make Release Readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making Release Readiness decisions. We found that reasons for failed Releases and the factors considered for making Release decisions are context specific and vary with Release cycle time. Practitioners confirmed that (i) Release Readiness should be measured and continuously monitored during the whole Release cycle, (ii) Release Readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed Releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of Release development were articulated as key requirements.

  • EASE - A Two-staged Survey on Release Readiness
    Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, 2017
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Maleknaz Nayebi, Guenther Ruhe
    Abstract:

    Deciding about the content and Readiness when shipping a new product Release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world Release decisions and to what extent research on Release Readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make Release Readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making Release Readiness decisions. We found that reasons for failed Releases and the factors considered for making Release decisions are context specific and vary with Release cycle time. Practitioners confirmed that (i) Release Readiness should be measured and continuously monitored during the whole Release cycle, (ii) Release Readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed Releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of Release development were articulated as key requirements.

  • Monitoring and Controlling Release Readiness by Learning Across Projects
    Managing Software Process Evolution, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Günther Ruhe
    Abstract:

    Releasing software on time, with desired quality while staying within budget is crucial for success. Therefore, product managers should proactively know which Release Readiness attributes are not performing sufficiently well (i.e., bottleneck factors) throughout the development cycle and consequently may limit Readiness of the software Release. We present the Cross-project Analysis for Selection of Release Readiness attributes (CASRR) method to help project managers in (i) systematically studying and analyzing Release Readiness attributes across multiple projects, (ii) selection of Release Readiness attributes for monitoring which have previously been shown to become bottlenecks in similar projects in the past, and (iii) learning how bottleneck occurrences are influenced by project characteristics. We applied CASRR to two Open Source Software projects, and analyzed six Release Readiness attributes in 34 similar projects over a period of two years. Continuous integration rate, feature completion rate, and bug fixing rate are observed as the most frequent bottleneck factors. Bottleneck occurrences of the monitored Release Readiness attributes are significantly influenced by the maturity of a Release. Furthermore, the continuous integration rate is found to be significantly influenced by the team size.

  • Release Readiness classification an explorative case study
    Empirical Software Engineering and Measurement, 2016
    Co-Authors: S Didar Al M Alam, Dietmar Pfahl, Guenther Ruhe
    Abstract:

    Context: To survive in a highly competitive software market, product managers are striving for frequent, incremental Releases in ever shorter cycles. Release decisions are characterized by high complexity and have a high impact on project success. Under such conditions, using the experience from past Releases could help product managers to take more informed decisions. Goal and research objectives: To make decisions about when to make a Release more operational, we formulated Release Readiness (RR) as a binary classification problem. The goal of our research presented in this paper is twofold: (i) to propose a machine learning approach called RC* (Release Readiness Classification applying predictive techniques) with two approaches for defining the training set called incremental and sliding window, and (ii) to empirically evaluate the applicability of RC* for varying project characteristics. Methodology: In the form of explorative case study research, we applied the RC* method to four OSS projects under the Apache Software Foundation. We retrospectively covered a period of 82 months, 90 Releases and 3722 issues. We use Random Forest as the classification technique along with eight independent variables to classify Release Readiness in individual weeks. Predictive performance was measured in terms of precision, recall, F-measure, and accuracy. Results: The incremental and sliding window approaches respectively achieve an overall 76% and 79% accuracy in classifying RR for four analyzed projects. Incremental approach outperforms sliding window approach in terms of stability of the predictive performance. Predictive performance for both approaches are significantly influenced by three project characteristics i) Release duration, ii) number of issues in a Release, iii) size of the initial training dataset. Conclusion: As our initial observation we identified, incremental approach achieves higher accuracy when Releases have long duration, low number of issues and classifiers are trained with large training set. On the other hand, sliding window approach achieves higher accuracy when Releases have short duration and classifiers are trained with small training set.

  • ESEM - Release Readiness Classification: An Explorative Case Study
    Proceedings of the 10th ACM IEEE International Symposium on Empirical Software Engineering and Measurement, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Guenther Ruhe
    Abstract:

    Context: To survive in a highly competitive software market, product managers are striving for frequent, incremental Releases in ever shorter cycles. Release decisions are characterized by high complexity and have a high impact on project success. Under such conditions, using the experience from past Releases could help product managers to take more informed decisions. Goal and research objectives: To make decisions about when to make a Release more operational, we formulated Release Readiness (RR) as a binary classification problem. The goal of our research presented in this paper is twofold: (i) to propose a machine learning approach called RC* (Release Readiness Classification applying predictive techniques) with two approaches for defining the training set called incremental and sliding window, and (ii) to empirically evaluate the applicability of RC* for varying project characteristics. Methodology: In the form of explorative case study research, we applied the RC* method to four OSS projects under the Apache Software Foundation. We retrospectively covered a period of 82 months, 90 Releases and 3722 issues. We use Random Forest as the classification technique along with eight independent variables to classify Release Readiness in individual weeks. Predictive performance was measured in terms of precision, recall, F-measure, and accuracy. Results: The incremental and sliding window approaches respectively achieve an overall 76% and 79% accuracy in classifying RR for four analyzed projects. Incremental approach outperforms sliding window approach in terms of stability of the predictive performance. Predictive performance for both approaches are significantly influenced by three project characteristics i) Release duration, ii) number of issues in a Release, iii) size of the initial training dataset. Conclusion: As our initial observation we identified, incremental approach achieves higher accuracy when Releases have long duration, low number of issues and classifiers are trained with large training set. On the other hand, sliding window approach achieves higher accuracy when Releases have short duration and classifiers are trained with small training set.

S. M. Didar Al Alam - One of the best experts on this subject based on the ideXlab platform.

  • EASE - A Two-staged Survey on Release Readiness
    Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, 2017
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Maleknaz Nayebi, Guenther Ruhe
    Abstract:

    Deciding about the content and Readiness when shipping a new product Release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world Release decisions and to what extent research on Release Readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make Release Readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making Release Readiness decisions. We found that reasons for failed Releases and the factors considered for making Release decisions are context specific and vary with Release cycle time. Practitioners confirmed that (i) Release Readiness should be measured and continuously monitored during the whole Release cycle, (ii) Release Readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed Releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of Release development were articulated as key requirements.

  • Monitoring and Controlling Release Readiness by Learning Across Projects
    Managing Software Process Evolution, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Günther Ruhe
    Abstract:

    Releasing software on time, with desired quality while staying within budget is crucial for success. Therefore, product managers should proactively know which Release Readiness attributes are not performing sufficiently well (i.e., bottleneck factors) throughout the development cycle and consequently may limit Readiness of the software Release. We present the Cross-project Analysis for Selection of Release Readiness attributes (CASRR) method to help project managers in (i) systematically studying and analyzing Release Readiness attributes across multiple projects, (ii) selection of Release Readiness attributes for monitoring which have previously been shown to become bottlenecks in similar projects in the past, and (iii) learning how bottleneck occurrences are influenced by project characteristics. We applied CASRR to two Open Source Software projects, and analyzed six Release Readiness attributes in 34 similar projects over a period of two years. Continuous integration rate, feature completion rate, and bug fixing rate are observed as the most frequent bottleneck factors. Bottleneck occurrences of the monitored Release Readiness attributes are significantly influenced by the maturity of a Release. Furthermore, the continuous integration rate is found to be significantly influenced by the team size.

  • ESEM - Release Readiness Classification: An Explorative Case Study
    Proceedings of the 10th ACM IEEE International Symposium on Empirical Software Engineering and Measurement, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Guenther Ruhe
    Abstract:

    Context: To survive in a highly competitive software market, product managers are striving for frequent, incremental Releases in ever shorter cycles. Release decisions are characterized by high complexity and have a high impact on project success. Under such conditions, using the experience from past Releases could help product managers to take more informed decisions. Goal and research objectives: To make decisions about when to make a Release more operational, we formulated Release Readiness (RR) as a binary classification problem. The goal of our research presented in this paper is twofold: (i) to propose a machine learning approach called RC* (Release Readiness Classification applying predictive techniques) with two approaches for defining the training set called incremental and sliding window, and (ii) to empirically evaluate the applicability of RC* for varying project characteristics. Methodology: In the form of explorative case study research, we applied the RC* method to four OSS projects under the Apache Software Foundation. We retrospectively covered a period of 82 months, 90 Releases and 3722 issues. We use Random Forest as the classification technique along with eight independent variables to classify Release Readiness in individual weeks. Predictive performance was measured in terms of precision, recall, F-measure, and accuracy. Results: The incremental and sliding window approaches respectively achieve an overall 76% and 79% accuracy in classifying RR for four analyzed projects. Incremental approach outperforms sliding window approach in terms of stability of the predictive performance. Predictive performance for both approaches are significantly influenced by three project characteristics i) Release duration, ii) number of issues in a Release, iii) size of the initial training dataset. Conclusion: As our initial observation we identified, incremental approach achieves higher accuracy when Releases have long duration, low number of issues and classifiers are trained with large training set. On the other hand, sliding window approach achieves higher accuracy when Releases have short duration and classifiers are trained with small training set.

  • Comparative Analysis of Predictive Techniques for Release Readiness Classification
    2016 IEEE ACM 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE), 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Muhammad Rezaul Karim, Günther Ruhe
    Abstract:

    Context: A software Release is the deployment of a version of an evolving software product. Product managers are typically responsible for deciding the Release content, time frame, price, and quality of the Release. Due to all the dynamic changes in the project and process parameters, the decision is highly complex and of high impact.Objective: This paper has two objectives: i) Comparative analysis of predictive techniques in classifying an ongoing Release in terms of its expected Release Readiness., and ii) Comparative analysis between regular and ensemble classifiers to classify an ongoing Release in terms of its expected Release Readiness.Methodology: We use machine learning classifiers to predict Release Readiness. We analyzed three OSS projects under Apache Software Foundation from JIRA issue repository. As a retrospective study, we covered a period of 70 months, 85 Releases and 1696 issues. We monitored eight established variables to train classifiers in order to predict whether Releases will be ready versus non-ready. Predictive performance of different classifiers was compared by measuring precision, recall, F-measure, balanced accuracy, and area under the ROC curve (AUC).Results: Comparative analysis among nine classifiers revealed that ensemble classifiers significantly outperform regular classifiers. Balancing precision and recall, Random Forrest and BaggedADABoost were the two best performers in total, while Naïve Bayes performed best among just the regular classifiers.

  • RAISE@ICSE - Comparative analysis of predictive techniques for Release Readiness classification
    Proceedings of the 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering - RAISE '16, 2016
    Co-Authors: S. M. Didar Al Alam, Dietmar Pfahl, Muhammad Rezaul Karim, Günther Ruhe
    Abstract:

    Context: A software Release is the deployment of a version of an evolving software product. Product managers are typically responsible for deciding the Release content, time frame, price, and quality of the Release. Due to all the dynamic changes in the project and process parameters, the decision is highly complex and of high impact. Objective: This paper has two objectives: i) Comparative analysis of predictive techniques in classifying an ongoing Release in terms of its expected Release Readiness., and ii) Comparative analysis between regular and ensemble classifiers to classify an ongoing Release in terms of its expected Release Readiness. Methodology: We use machine learning classifiers to predict Release Readiness. We analyzed three OSS projects under Apache Software Foundation from JIRA issue repository. As a retrospective study, we covered a period of 70 months, 85 Releases and 1696 issues. We monitored eight established variables to train classifiers in order to predict whether Releases will be ready versus non-ready. Predictive performance of different classifiers was compared by measuring precision, recall, F-measure, balanced accuracy, and area under the ROC curve (AUC). Results: Comparative analysis among nine classifiers revealed that ensemble classifiers significantly outperform regular classifiers. Balancing precision and recall, Random Forrest and BaggedADABoost were the two best performers in total, while Naive Bayes performed best among just the regular classifiers.

Philipp Brune - One of the best experts on this subject based on the ideXlab platform.

  • determining software product Release Readiness by the change error correlation function on the importance of the change error time lag
    Hawaii International Conference on System Sciences, 2012
    Co-Authors: Roman Wild, Philipp Brune
    Abstract:

    In software development determining the Release Readiness plays an essential role. The number of errors is frequently used as an important measure to decide about the quality of a software implementation. Therefore, error prediction techniques have been intensively studied in the literature for many years. Despite this, their adoption in practice is still strongly limited to date. In this paper, an alternative model for error prediction in software projects based on linear response theory and the change-error cross correlation function is proposed. It is applied to data collected in projects of a major embedded systems vendor in the communication industry. Under similar conditions, a universal behavior of the change-error cross-correlation function is observed. Moreover, a time lag of 4-6 weeks between the change and the detection of related errors is discovered. This clearly demonstrates that for reliable Release decisions not only the current number of errors but also of changes is essential.

  • HICSS - Determining Software Product Release Readiness by the Change-Error Correlation Function: On the Importance of the Change-Error Time Lag
    2012 45th Hawaii International Conference on System Sciences, 2012
    Co-Authors: Roman Wild, Philipp Brune
    Abstract:

    In software development determining the Release Readiness plays an essential role. The number of errors is frequently used as an important measure to decide about the quality of a software implementation. Therefore, error prediction techniques have been intensively studied in the literature for many years. Despite this, their adoption in practice is still strongly limited to date. In this paper, an alternative model for error prediction in software projects based on linear response theory and the change-error cross correlation function is proposed. It is applied to data collected in projects of a major embedded systems vendor in the communication industry. Under similar conditions, a universal behavior of the change-error cross-correlation function is observed. Moreover, a time lag of 4-6 weeks between the change and the detection of related errors is discovered. This clearly demonstrates that for reliable Release decisions not only the current number of errors but also of changes is essential.

Miroslaw Staron - One of the best experts on this subject based on the ideXlab platform.

  • assessing the Release Readiness of engine control software
    2018 IEEE ACM 1st International Workshop on Software Qualities and their Dependencies (SQUADE), 2018
    Co-Authors: Sichao Wen, Christoffer Nilsson, Miroslaw Staron
    Abstract:

    Powertrain control calibration is an essential stage before the delivery of final products, to ensure that a vehicle works well in all driving environments. However, due to complexity of a powertrain control system (often as many as 40,000 parameters), it is difficult to assess when all parameters are fully calibrated. Therefore, the aim of this paper is to explore an approach to evaluate and predict the maturity level of powertrain control software based on calibration data for simulation models. We developed metrics that indicate software maturity and their visualization using heatmaps. We used software maturity growth curves to select maturity growth models for maturity assessment. The results and approaches of this paper were validated with theoretical methods and empirical methods using action research techniques. Our conclusions show that we can use standard software reliability growth models to monitor the calibration process for powertrain software, and we conclude that this type of monitoring provides a good support for quality stakeholders in monitoring powertrain calibration quality.

  • SQUADE@ICSE - Assessing the Release Readiness of engine control software
    Proceedings of the 1st International Workshop on Software Qualities and Their Dependencies, 2018
    Co-Authors: Sichao Wen, Christoffer Nilsson, Miroslaw Staron
    Abstract:

    Powertrain control calibration is an essential stage before the delivery of final products, to ensure that a vehicle works well in all driving environments. However, due to complexity of a powertrain control system (often as many as 40,000 parameters), it is difficult to assess when all parameters are fully calibrated. Therefore, the aim of this paper is to explore an approach to evaluate and predict the maturity level of powertrain control software based on calibration data for simulation models. We developed metrics that indicate software maturity and their visualization using heatmaps. We used software maturity growth curves to select maturity growth models for maturity assessment. The results and approaches of this paper were validated with theoretical methods and empirical methods using action research techniques. Our conclusions show that we can use standard software reliability growth models to monitor the calibration process for powertrain software, and we conclude that this type of monitoring provides a good support for quality stakeholders in monitoring powertrain calibration quality.

  • selecting software reliability growth models and improving their predictive accuracy using historical projects data
    Journal of Systems and Software, 2014
    Co-Authors: Rakesh Rana, Wilhelm Meding, Miroslaw Staron, Martin Nilsson, Christian Berger, Jorgen Hansson, Fredrik Torner, Christoffer Hoglund
    Abstract:

    8 software reliability growth models are evaluated on 11 large projects.Logistic and Gompertz models have the best fit and asymptote predictions.Using growth rate from earlier projects improves asymptote prediction accuracy.Trend analysis allows choosing the best shape of the model at 50% of project time. During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for Release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing Release Readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model.

  • IWSM/Mensura - Consequences of Mispredictions of Software Reliability: A Model and its Industrial Evaluation
    2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measureme, 2014
    Co-Authors: Miroslaw Staron, Wilhelm Meding, Rakesh Rana, Martin Nilsson
    Abstract:

    Predicting reliability of software under development is an important part of estimations in software engineering projects. In many organizations as the goal is that software products are Released with no known defects, the process of finding and removing defects correlates with the effort for software projects. Software development projects estimate the resources needed to design, develop, test and Release software products, and the number of defects which have to be handled. In this paper we present a model for consequence analysis of inaccurate predictions of quality in software projects. The model is a result of multiple case studies and is evaluated at two companies. The model recognizes the most common mispredictions - e.g. Over- and under-prediction, early- and late-predictions - and the combination of theses. The results from the industrial evaluation show that the consequences can be grouped according to under- and over-predictions and that the late- and early-predictions have the same consequences. The results show also that mispredicting the shape of the reliability curve has a significant consequence with regard to assessment of Release Readiness and resource planning.

  • Release Readiness indicator for mature agile and lean software development projects
    International Conference on Agile Software Development, 2012
    Co-Authors: Miroslaw Staron, Wilhelm Meding, Klas Palm
    Abstract:

    Large companies like Ericsson increasingly often adopt the principles of Agile and Lean software development and develop large software products in iterative manner – in order to quickly respond to customer needs. In this paper we present the main indicator which is sufficient for a mature software development organization in order to predict the time in weeks to Release the product. In our research project we collaborated closely with a large Agile+Lean software development project at Ericsson in Sweden. This large and mature software development project and organization has found this main indicator – Release Readiness – to be so important that it was used as a key performance indicator and is used in controlling the development of the product and improving organizational performance. The indicator was developed and validated in an action research project at one of the units of Ericsson AB in Sweden in one of its largest projects.