It is our great pleasure to welcome you to PROMISE 2008 - the 4th International Workshop on Predictor Models in Software Engineering. This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports in the area of predictor models applied to software engineering. The theme for this year's workshop is Bridging Research and Industry. Key questions for the workshop are -- How might PROMISE and other researchers better align with the realities of industry? -- How can industry make effective use of research ideas?
In keeping with this theme, we have two keynote speakers from industry. Dr. Murray Cantor is an IBM Distinguished Engineer and the governance solutions lead on the IBM Rational Software CTO team. Mr. Chris Beal is a Sun Microsystems Senior Staff Engineer working with Solaris Revenue Product Engineering.
We are pleased that PROMISE has become an international event. Our call for papers attracted submissions from Asia, Canada, Europe, and the United States. The program committee accepted over a dozen papers covering a variety of topics, including models related to fault prediction, effort estimation, and requirements engineering.
One feature that sets this workshop apart from others is the PROMISE repository of software data sets that are publicly available for research purposes. The repository currently has 57 data sets and has grown at an average rate of 44% annually over the last 3.5 years.
Proceeding Downloads
Comparing negative binomial and recursive partitioning models for fault prediction
Two different software fault prediction models have been used to predict the N% of the files of a large software system that are likely to contain the largest numbers of faults. We used the same predictor variables in a negative binomial regression ...
Comparing design and code metrics for software quality prediction
The prediction of fault-prone modules continues to attract interest due to the significant impact it has on software quality assurance. One of the most important goals of such techniques is to accurately predict the modules where faults are likely to ...
Adapting a fault prediction model to allow inter languagereuse
An important step in predicting error prone modules in a project is to construct the prediction model by using training data of that project, but the resulting prediction model depends on the training data. Therefore it is difficult to apply the model ...
An empirical analysis of software effort estimation with outlier elimination
Accurate software effort estimation has always been challenge for software engineering communities. To improve the estimation accuracy of software effort, many studies have focused on effort estimation methods without any consideration of data quality, ...
Using correlation and accuracy for identifying good estimators
Human-based estimation remains the predominant methodology of choice [1]. Understanding the human estimator is critical for improving the effort estimation process. Every human estimator draws upon their background in terms of domain knowledge, ...
Data sets and data quality in software engineering
OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engineering data sets. We consider this a particularly interesting question in the context of initiatives to promote sharing and secondary analysis of data ...
Implications of ceiling effects in defect predictors
Context: There are many methods that input static code features and output a predictor for faulty code modules. These data mining methods have hit a "performance ceiling"; i.e., some inherent upper bound on the amount of information offered by, say, ...
Multi-criteria decision analysis for customization of estimation by analogy method AQUA+
The quality of results from a predictor model depends on the proper customization of the parameters of the model. For Estimation by Analogy (EBA), the impact of the parameter "Attribute weighting technique" has been shown by several authors. The ...
Confidence in software cost estimation results based on MMRE and PRED
Bootstrapping is used to approximate the standard error and 95% confidence intervals of MMRE and PRED for a number of COCOMO I model variations applied to four PROMISE data sets. This is used to illustrate a lack of confidence in numerous published cost ...
Improving analogy software effort estimation using fuzzy feature subset selection algorithm
One of the major problems with software project management is the difficulty to predict accurately the required effort for developing software applications. Analogy Software effort estimation appears well suited to model problems of this nature. The ...
Optimizing requirements decisions with keys
Recent work with NASA's Jet Propulsion Laboratory has allowed for external access to five of JPL's real-world requirements models, anonymized to conceal proprietary information, but retaining their computational nature. Experimentation with these models,...
Complementing approaches in ERP effort estimation practice: an industrial study
Projects implementing enterprise resource planning (ERP) solutions are characterized by specific context factors such as high level of reuse, scope of the ERP modules, interdependent functionality, and use of vendor-specific standard implementation ...
Software defect repair times: a multiplicative model
We analyzed over 10,000 software defect repair times collected for nine products at Cisco Systems, to confirm our hypothesis that software defect repair times can be characterized by the Laplace Transform of the Lognormal (LTLN) distribution. This ...
Risk and relevance
Conventional wisdom is that incomplete development efforts have no value. This accountant-driven approach precludes reasoning about the ongoing return on investment for ongoing development efforts. This talk lays out a framework for quantifying the ...
Practical use of defect detection and prediction in the development and maintenance of software
Large complex software products inevitably contain software defects. Some of these are identified before the product ships. In the likely case where the number of defects exceeds the engineering resource required to fix them, it is important to identify ...
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
PROMISE | 25 | 12 | 48% |
PROMISE 2016 | 23 | 10 | 43% |
PROMISE '15 | 16 | 8 | 50% |
PROMISE '14 | 21 | 9 | 43% |
PROMISE '12 | 24 | 12 | 50% |
PROMISE '08 | 16 | 13 | 81% |
Overall | 125 | 64 | 51% |