Zeeshan Ali Rana

Publications (5)0 Total impact

• Article: Nomenclature unification of software product measures.

  Zeeshan Ali Rana, Mian M. Awais, Shafay Shamail
  IET Software. 01/2011; 5:83-102.
• Conference Proceeding: An FIS for Early Detection of Defect Prone Modules.

  Zeeshan Ali Rana, Mian M. Awais, Shafay Shamail
  Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009, Proceedings; 01/2009
• Source

  Available from: suraj.lums.edu.pk

  Chapter: An FIS for Early Detection of Defect Prone Modules

  Zeeshan Ali Rana, Mian Muhammad Awais, Shafay Shamail
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  ABSTRACT: Early prediction of defect prone modules helps in better resource planning, test planning and reducing the cost of defect correction in later stages of software lifecycle. Early prediction models based on design and code metrics are difficult to develop because precise values of the model inputs are not available. Conventional prediction techniques require exact inputs, therefore such models cannot always be used for early predictions. Innovative prediction methods that use imprecise inputs, however, can be applied to overcome the requirement of exact inputs. This paper presents a fuzzy inference system (FIS) that predicts defect proneness in software using vague inputs defined as fuzzy linguistic variables. The paper outlines the methodology for developing the FIS and applies the model to a real dataset. Performance analysis in terms of recall, accuracy, misclassification rate and a few other measures has been conducted resulting in useful insight to the FIS application. The FIS model predictions at an early stage have been compared with conventional prediction methods (i.e. classification trees, linear regression and neural networks) based on exact values. In case of the FIS model, the maximum and the minimum performance shortfalls were noticed for true negative rate (TN Rate) and F measure respectively. Whereas for Recall, the FIS model performed better than the other models even with the imprecise inputs.
  01/1970: pages 144-153;
• Article: Ineffectiveness of Use of Software Science Metrics as Predictors of Defects in Object Oriented Software (PDF)

  Zeeshan Ali Rana, Shafay Shamail, Mian Muhammad Awais
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  ABSTRACT: Software science metrics (SSM) have been widely used as predictors of software defects. The usage of SSM is an effect of correlation of size and complexity metrics with number of defects. The SSM have been proposed keeping in view the procedural paradigm and structural nature of the programs. There has been a shift in software development paradigm from procedural to object oriented (OO) and SSM have been used as defect predictors of OO software as well. However, the effectiveness of SSM in OO software needs to be established. This paper investigates the effectiveness of use of SSM for: a)classification of defect prone modules in OO software b) prediction of number of defects. Various binary and numeric classification models have been applied on dataset kc1 with class level data to study the role of SSM. The results show that the removal of SSM from the set of independent variables does not significantly affect the classification of modules as defect prone and the prediction of number of defects. In most of the cases the accuracy and mean absolute error has improved when SSM were removed from the set of independent variables. The results thus highlight the ineffectiveness of use of SSM in defect prediction in OO software.
  Software Engineering, World Congress on. 4:3-7.
• Source

  Available from: suraj.lums.edu.pk

  Article: Towards a generic model for software quality prediction

  Zeeshan Ali Rana, Shafay Shamail, Mian Muhammad Awais
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  ABSTRACT: Various models and techniques have been proposed and applied in literature for software quality prediction. Specificity of each suggested model is one of the impediments in development of a generic model. A few models have been quality factor specific whereas others are software development paradigm specific. The models can even be company specific or domain specific. The amount of work done for software quality prediction compels the researchers to get benefit from the existing models and develop a relatively generic model. Development of a generic model will facilitate the quality managers by letting them focus on how to improve the quality instead of employing time on deciding which technique best suites their scenario. This paper suggests a generic model which takes software as input and predicts a quality factor value using existing models. This approach captures the specificity of existing models in various dimensions (like quality factor, software development paradigm, and software development life cycle phase etc.), and calculates quality factor value based on the model with higher accuracy. Application of the model has been discussed with the help of an example.