Evaluation of Code Smells Detection Using Meta-heuristics

Evaluation of Code Smells Detection Using Meta-heuristics Evaluation of code smells detection using Meta-heuristics  Optimization algorithm Ragulraja.M Abstract-The development of software systems over many years leads to needless complexity and inflexibility in  design which leads to a large amount of effort for enhancements and maintenance. To take code smells detection as a  distributed optimization problem. The intention is that to aggregates different methods in parallel way to achieve a  common goal detection of code smells. To this conclusion, it utilized Parallel Evolutionary algorithms (P-EA) where  numerous evolutionary algorithms with adaptation are executed in parallel cooperative manner, to find unanimity  between detection of code smells. An experimental results to compare the execution of our cooperative P-EA method with  random search, two genetic based approaches and two bad designs detection techniques are found to provide the  statistical measure of results witness to support the claim that cooperative P-EA is more economic and potential than the  art detection approaches based on benchmark of open source systems, whereas the results are generated in terms of  precision and recall incurred on various code smells types. In this approach should corroborate on an extra code smells  types with the objective of resolve the common applicability of our methodology. Keywords-Parallel Evolutionary Algorithm, Software Metrics, Code smells, Software Quality Engineering. I.INTRODUCTION Software maintenance projects are very  costly. The total maintenance costs of Software  project are estimated to 40%-70% of the total cost of the lifecycle of the project consequently, reducing the  effort spent on maintenance can be seen as a natural  way of reducing the overall costs of a software  project. This is one of the main reasons for the recent  interest in concepts such as refactoring and code  smells. Hence, researchers have proposed several  approaches to reduce defects in software .Suggested  solutions include improvement of clarity in software  design, effective use of process and product metrics,  achievement of extensibility and adaptability in the  development process. The research focusing on the  study of bad software designs also called bad smells  or code smells. To avoid these codes smells  developers to understand the structure of source code. The large systems of existing work in bad  smells or code smells detection relies on declarative  rule specification. In these specifications, rules are  manually constructed to identify symptoms that can  be used for categorization code smells with object  oriented metrics information. Each code smell, rules  are defined in the form of metrics combinations. Many studies reported that manual categorization  with declarative rule specification can be large. These  need a threshold value to specify the code smells. Further problem is that translation from symptoms to  rules is not obvious because there is no unanimity  symptom based description of bad smells. When unanimity occurs, the correlation of symptoms could  be consociated with code smells types, it leads to  precise identification of code smells types. To handle these problems, we plan to extend  an approach based on use of genetic programming to  provide detection rules from the examples of code  smells detection with metric combinations. However,  the quality of the rules depends on the behavioral  aspects of code smells, and it is not easy to confirm  that coverage also because there is still some  precariousness involves in detected code smells due  to the difficulty to evaluate the coverage of the base  of code smell examples. In another past work, we proposed technique  based on an artificial immune system metaphor to  detect code smells by deviation with well designed  systems. Thus, we believe in that an effective method  will be to merge with detection algorithms to  discover consensus when detecting code smells. We intend to provide code smells detection as a  distributed optimization problem.The implementation  of our approach can be established by combining  Optimization process in parallel manner to encounter  consensus involving detection of code smells. II. RELATED WORKS: There are various studies that have mainly  based on the code smells detection in software  engineering using different methods. These  methodologies range from fully automatic detection  to direct manual inspection. However,there is no  work that focuses on merging various detection  algorithms to find unanimity when identifying code  smells. In this work, the classification existing  approach for detection of code smells into various  broad categories: symptom based approaches, manual  approaches, metric based approaches, search based  approaches and cooperative based approaches. 2.1 Manual approaches: The software maintainers should manually  inspect the program to detect existing code  anomalies. In addition, they mentioned particular  refactoringà ¢Ã¢â€š ¬Ã… ¸s for each code smells type. The  technique is to create a set of â€Å"reading techniques†Ã‚  which help a reviewer to â€Å"read† a design artifact for  calculating related information. The demerits of  existing manual approaches is that they are finally a  human centric process which involves a great human  effort and strong analysis and interpretation attempt  from software maintainers to find design fragments  that are related to code smells.Furthermore, these  methods are time consuming, error prone and focus  on programs in their contexts. Another significant  issue is that locating code smells manually has been  prescribed as more a human intuition than an accurate  science. 2.2 Metric based approaches: The â€Å"detection strategy†mechanism for  formulating metric based rules for finding deviations  from well design code. Detection strategies permits to  maintainer to directly find classes or methods  subjected by a particular design smells. These  detection strategies for capturing about ten important  flaws of object oriented design found in literature. It  is accomplished by evaluating design quality of an  object oriented system via quantifying deviations  from good design heuristics and principles by  mapping these design defects to class level metrics  such as complexity, coupling and cohesion by defining rules. Unfortunately, multi metrics neither  encapsulate metrics in a more abstract construct,nor  do they permit a negotiable combination of metrics. In common, the effectiveness of combining metric or  threshold is not clear, that is for each code smell,  rules that are declared in terms of metric  combinations need an important calibration effort to  find the fixing of threshold values for each metric. 2.3 Search based approaches: This approach is divined by contributions in  the domain of search based software engineering. SBSE uses search based approaches to resolve  optimizations problems in software engineering. Once the task is consider as a search problem, several  search algorithms can be employed to solve that  problem. Another approach is based on search based  techniques, for the automatic identification of  potential code smells in code. The detection focused  on thenotion that more code deviates from good  codes, the more likely it is bad. In another work,  detections rule will be produced and is described as a  combination of metrics or thresholds that better  similar to known an examples of bad smells. Then,  the correction solutions, a combination of refactoring  operations, should reduce the number of bad smells  detected using the detection rules. 2.4 Cooperative based approaches: Some cooperative approaches to reference  software engineering problems have been proposed  recently, in this program and test cases co-evolve,  regulating each other with the aim of fixing the  maximum number of bugs in the programs. The  objective is to improve the effectiveness of obtained  test cases by evaluating their capabilities to avoid  mutants.The P-EA proposal is vary from existing coevolutionary  approaches, this proposal based on two  populations that are referencing the same problem  from various perspectives. Finally, the genetic based  approaches are executed in parallel in our P-EA  framework. III. PROPOSED SCHEME In this paper, we suggested a new search  based approach for detection of code smells. In this  approach a parallel metaheuristic optimization  algorithm adaptation, two genetic populations are  involves simultaneously with the target of each  depending on the current population of other in a  parallel cooperative manner. Both populations are  generated, on the similar open source systems to  evaluate, and the solutions are punished based on the  intersection between the results of two populations  are found. We extend our approach to various code  smells types in order to resolve about common  applicability ofcooperative parallel search based  software engineering. Moreover, in this work we not  only focus on the detection of code smells but also  concentrate automated the correction of code smells. Furthermore, in this paper we consider the  essential need of code smells during the detection  procedure using existing code changes, classes and  coupling complexity. Hence, the detected code smells  will be ranked based on the severity score and also an  important score. We will measure also the use of  more than two algorithms executed in parallel  manner as a part our work to generate results of more  accuracy than art detection approach. The negative  impact on the code smells can be removed by  applying more than two algorithms in cooperative  manner ità ¢Ã¢â€š ¬Ã… ¸s difficult to find the consensus between  the code smells. The research work will direct our  approach to several software engineering problems  such as software testing and quality assurance. IV. PROPOSED ARCHITECTURE Fig 1:system architecture 1. Metrics Evaluation 2. Evolutionary Algorithms 3. Code Smell Detection 4.1 METRICS EVALUATION 4.1.1 CK METRIC SUITE Chidember and kemerer proposed a six metric  suite used for analyzing the proposed variable. The six  metric suite are: 1. Weighted Method Per Class(WMC): Consider a class C1 with methods M1†¦.Mn  that are included in class. Let C1,C2†¦Cn be the sum of  complexity. WMC=ÃŽ £ M 2. Depth Of Inheritance(DIT): The maximum length from the node to the  root of the tree. 3. Number Of Children(NOC): Number of immediate subclasses subordinated  to a class in the class hierarchy. 4. Coupling Between Objects(CBO): It is a count of the number of other classes to  which it is coupled. 5. Response For a Class (RFC) It is the number of methods of the class plus  the number of methods called by any of those  methods. 4.1.2 Lack Of Cohesion of Methods (LCOM)  Measure the dissimilarity of methods in a  class via instanced variables. 4.2 EVOLUTIONARY ALGORITHMS The fundamental think of both algorithms is  to explore the search space by devising a population  of candidate solutions, also called individuals,  germinate towards a â€Å"good† solution of a unique  problem. To measure the solutions, the fitness  function in both algorithms has two components. For  the first component of the fitness function, GP  evaluates the detection rules based on the coverage of  code-smells examples. In GP, a solution is combined  of terminals and functions. Hence, while applying GP  to clear particular problem, they should be carefully  collected and fashioned to fulfil the requirements of  the current problem. Afterwards, evaluating large  parameters concerned to the code-smells detection  problem, the terminal set and the function set are  recognized as follows. The terminals fit to different  quality metrics with their threshold values (constant  values). The functions that can be used between these  metrics ar e Union (OR) and Intersection (AND). The second algorithm run in parallel is  genetic algorithm that generates detectors from welldesigned  code examples. For GA, detectors defend  generated artificial code fragments dignified by code  elements. Thus, detectors are mentioned as a vector  where each dimension is a code element. We defend  these elements as sets of predicates. All predicate  type represents to a construct type of an objectoriented  system. Then, a set of best solutions are  collected from P-EA algorithms in each iteration,  Bothalgorithms interact with one other victimizing  the second component of the fitness function called  intersection function. 4.3 CODE SMELLS DETECTION Code smells are design flaws that can be  solved by refactoringà ¢Ã¢â€š ¬Ã… ¸s. They are considered as flags  to the developer that some parts of the design may be  inappropriate and that it can be improved. For the  purpose of this work, we discuss a few representative  code smells. There are a lot of code smells mentioned  in the development of this work. A thorough catalog  of code smells can be found in Fowlers refactoring  book. As this work focuses on program analysis, code smells discussed in this work include those that  require analyses. Though this work develops only a  subset of the code smells, it provides some grounds  which can be adapted to other types of code smells. The set of best solutions from each algorithm is  stored and a new population of individuals is  generated by repetitively choosing pairs of parent  individuals from population p and employing the  crossover operator to them. We admit both the parent  and child variants in the new population pop. Then,  we apply the mutation operator, with a probability  score, for both parent and child to assure the solution  diversity; this produces the population for the next  generation. While applying change operators, no  individuals are transformed between the parallel  GA/GP. Both algorithms exit when the termination  criterion is met, and issue the best set of rules and  detectors. At last, developers can use the best rules  and detectors to find code-smells on new system to  evaluate. V. EXPERIMENTAL RESULTS Fig 2: The impact of the nmber of code smell example on detection  results Fig 3: Average execution time comparison on the different system. VI. THREATS TO VALIDITY: Conclusion validity related with the  statistical relationship between the treatment and  outcome. The Wilcoxon rank sum test was used with  a 95 percent confidence level to test its important  differences exist between the measurements for  different treatments. This test makes no supposition  that the data is normally distributed and is suitable for  ordinal data, so we can be assured that the statistical  relationships observed are significant. The  comparison with other techniques not based on  heuristic search; consider the parameters obtained  with the tools. This can be regarded as a threat that  can be addressed in the future by developing the  impact of various parameters on the quality of results  of DÉCOR and JDeodorant. Internal validity is related with the casual  relationship between the treatment and outcome. To  consider the internal threats to validity in the  utilization of stochastic algorithms since this  experimental work based on 51 independent  simulation runs for each problem instance and the  obtained results are statistically analyzed by using the  Wilcoxon rank sum test with a 95 percent fair  comparison between CPU times. VII. CONCLUSION AND FUTURE WORK In this approach a parallel metaheuristic  optimization algorithm adaptation, two genetic  populations are involves simultaneously with the  target of each depending on the current population of  other in a parallel cooperative manner. Both  populations are generated, on the similar open source  systems to evaluate, and the solutions are punished  based on the intersection between the results of two  populations are found.Moreover, in this work we not  only focus on the detection of code smells but also  concentrate automated the correction of code  smells.Furthermore, in this paper we consider the  essential need of code smells during the detection  procedure using existing code changes, classes and  coupling complexity. Hence, the detected code smells  will be ranked based on the severity score and also an  important score. We will measure also the use of  more than two algorithms executed in parallel  manner as a part our work to generate result s of more  accuracy than art detection approach. Future work  should corroborate our method with remaining code  smell types with the objective conclude about the  common applicability of our methodology. We will  assess also the use of more than the algorithm  accomplish simultaneously as a part of our rest of our  future work. Another future issue direction attached  to our approach is to adapt our cooperative parallel  evolutionary approach to various software  engineering problems such as software testing and  the following release problem. VIII. REFERENCES 1) WaelKessentini,MarouaneKessentini,HouariSahrao  ui, Slim Bechikh:†A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection† IEEE Trans. Softw. Eng.,vol. 40,  no. 9, Sep 2014. 2) N. Moha, Y. G. Gu_eh_eneuc, L. Duchien, and A.  F. Le Meur, â€Å"DECOR: A method for the specification  and detection of code and design smells,† IEEE  Trans. Softw. Eng., vol. 36, no. 1, pp. 20–36,  Jan./Feb. 2010. 3) Chidamber, S., Kemerer, C.: „A metrics suite for  object oriented designà ¢Ã¢â€š ¬Ã… ¸,IEEE Trans. Softw. Eng.,  1994, 20, (6), pp. 476–493.   4) Mark Harman and AfshinMansouri.:†Search Based  Software Engineering: Introduction to the Special  Issue of the IEEE Transactions on Software  Engineering†,† IEEE Trans. Softw. Eng., vol. 36, no.  6,Nov./Dec. 2010.   5) F. Khomh, S. Vaucher, Y. G. Gu_eh_eneuc, and H.A. Sahraoui, â€Å"A bayesian approach for the detection  of code and design smells,† in Proc. Int. Conf.  Quality Softw., 2009, 305–314. 6) R. Marinescu, â€Å"Detection strategies: Metrics-based  rules for detecting design flaws,† in Proc. 20th Int.  Conf. Softw. Maintenance, 2004, pp. 350–359. 7) M. Kessentini, W. Kessentini, H. A. Sahraoui, M.  Boukadoum, and A. Ouni, â€Å"Design defects  detection and correction by example,† in Proc. IEEE  19th Int. Conf. Program Comprehension, 2011, pp.  81–90. 8) T. Burczy_nskia, W. Ku_sa, A. D »ugosza, and P.  Oranteka,â€Å"Optimization and defect identification  using distributed evolutionary algorithms,† Eng.  Appl. Artif. Intell., vol. 4, no. 17, pp. 337–344, 2004. 9) A. Ouni, M. Kessentini, H. A. Sahraoui, and M.  Boukadoum, â€Å"Maintainability defects detection and  correction: A multiobjective approach,† Autom.  Softw. Eng., vol. 20, no. 1, pp. 47–79, 2012. 10) O. Ciupke, â€Å"Automatic detection of design  problems in objectoriented reengineering,† in Proc.  Int. Conf. Technol. Object-OrientedLanguage Syst.,  1999, pp. 18–32. 12) G. Travassos, F. Shull, M. Fredericks, and V. R.  Basili, â€Å"Detecting defects in object-oriented designs:  Using reading techniques to increase software  quality,† in Proc. Int. conf. Object-Oriented  Program.,Syst., Languages, Appl., 1999, pp. 47–56. 13) M. Harman, S. A. Mansouri, and Y. Zhang,  Ã¢â‚¬Å"Search-based software engineering: Trends,  techniques and applications,† ACM Comput. Surv.,  vol. 45, no. 1, 61 pages. 14) A. Arcuri, X. Yao, â€Å"A novel co-evolutionary  approach to automatic software bug fixing,† in Proc.  IEEE Congr. Evol. Comput., 2008, pp. 162–168. 15) M. J. Munro, â€Å"Product metrics for automatic  identification of „Bad Smellà ¢Ã¢â€š ¬Ã… ¸ design problems in Java  source-code,† in Proc. IEEE 11th Int. Softw. Metrics  Symp., 2005, pp. 15–15.   16) W. Banzhaf, â€Å"Genotype-phenotype-mapping and  neutral variation: A case study in genetic  programming,† in Proc. Int. Conf. Parallel Problem  Solving from Nature, 1994, pp. 322–332. 17) W. H. Kruskal and W. A. Wallis, â€Å"Use of ranks in  one-criterion variance analysis,† J. Amer. Statist.  Assoc., vol. 47, no. 260, pp. 583–621, 1952. 18) W. J. Brown, R. C. Malveau, W. H. Brown, and  T. J. Mowbray, â€Å"Anti Patterns: Refactoring Software,  Architectures, and Projects in Crisis†. Hoboken, NJ,  USA: Wiley, 1998. 19) N. Fenton and S. L. Pfleeger, â€Å"Software Metrics:  A Rigorous and Practical Approach†. Int. Thomson  Comput. Press, London, UK, 1997.   20) Emerson Murphy-Hill, Chris Parnin, and Andrew  P. Black† How We Refactor, and How We Know  It†,IEEE Trans. Softw. Eng.,vol. 38,no. 1, Jan./Feb.  2012. 21) M. Fowler, K. Beck, J. Brant, W. Opdyke, and D.  Roberts, â€Å"Refactoring: Improving the Design of  Existing Code†. Reading, MA,USA: Addison  Wesley, 1999.

Dim Lighting Co. Essay

1. Problems Company is not in a position to spend the capital necessary to fund the project. The potential resignation of Robert Spinks if the project is not funded. If the project were to be funded, the extended time for development and the 30% chance that it might not be a success. 2. Causes Organizational culture is not consistent throughout all the departments. Accounting and manufacturing departments focus on increasing profits while R&D and marketing departments are open to new innovation and growth. Management is reactive rather than proactive. The company has not produced a new product in the past to capture the market. The history of Robert Spinks leaving companies due to the lack of creativity. 3. System affected The structural system is affected by not being encouraging towards innovation. The psychosocial system is affected by other departments being intimidated by Robert Spinks. Jim West is under pressure to improve profits. The technical system is affected because the technology of the Micro-miniaturization of lighting sources could bring about major  innovation. 4. Alternatives Ignore the concerns of the accounting and manufacturing departments and go ahead with the micro-miniaturization of lighting sources project. Discard the micro-miniaturization of lighting sources project and invest on the improvements of the current equipment. 5. Recommendations Invest capital on the new micro-miniaturization of light sources project. Although there is a considerable amount of time until the benefits can be reaped, such an innovation will be helpful to defeat the competition and capture the market. If Robert Spinks leaves the company and takes this technology to a competitor, Din Lighting Co. will be seriously affected.

Analyse the dramatic effectiveness in Act 3, Scene 5 Essay

Looking closely at the characters and language in Romeo and Juliet, analyse the dramatic effectiveness in Act 3, Scene 5 William Shakespeare wrote â€Å"Romeo and Juliet† in 1954, although the basic plot can be traced back as early as the third century. In the play, Shakespeare relies heavily on the poem â€Å"The Tragicall History of Romeus and Juliet† by Arthur Brooke. Most of the people in the Elizabethan era were perceptive enough to concentrate on how the play was being performed and engaged themselves in the language the characters were using. Shakespeare’s audiences had different expectations towards his play, as many of them recognised the story already, they were settled enough to watch it providing the dramatist’s interpretation proved to be unique and original. I have been looking closely at Act 3, Scene 5 where Romeo and Juliet have just been secretly married. The scene opens with the two lovers having to part quickly after the Nurse informs Juliet her mother is swiftly approaching. Already a dramatic atmosphere is created, the audience is almost waiting for Romeo and Juliet to be caught out, this they know can simply not happen. Juliet is understandably tearful; Romeo is sympathetic towards her, showing he really cares for her: â€Å"I will omit no opportunity That will convey my greetings, love, to thee† All this is in comparison to later scenes in the play showing Juliet solitary and unsupported. Between the two lovers, there is a great difference, Romeo appears more optimistic than Juliet who is full of fear, sensing premonitions of her next seeing Romeo dead in a tomb. Her premonitions affect the audience, making them apprehensive and tense: â€Å"O God, I have an ill- divining soul! Methinks I see thee, now thou art so low As one dead in the bottom of a tomb. † The audience hears these harsh, severe words and are reminded of Romeo’s earlier startling premonition that he would die young: â€Å"†¦. My mind misgives Some consequence not yet hanging in the stars†¦.. By come vile forfeit of untimely death. † A chilling effect is created on those viewing the play as they start to realise and understand the significance of the two premonitions. By looking back into earlier scenes, dramatic effectiveness is created. Juliet uses language that shows how she is fearful of how her life with Romeo could easily be destroyed. She speaks to him strongly, showing a strong contrast to her soft words used previously. The strong bond that has been created between the two lovers before the audience’s eyes is momentarily going to be destroyed; tension is created as an aftermath of this feeling. This tension carries on and becomes hugely greater as the news of County Paris’ proposal is first heard of. The audience watch, already aware of the proposal, as the news is given to an extremely shocked Juliet. They wait anxiously for Juliet’s sake as she learns of it, and so a dramatic effectiveness is cast over them. The scene is made effective by the use of irony from Lady Capulet. As Lady Capulet refers to her â€Å"joyful tidings† and Juliet’s response is ironically a pleased one: â€Å"And joy comes well in such a needy time† But then the audience sees the real reason of Lady Capulet’s announcement and the hesitation of the crucial words proves to be highly dramatic, â€Å"Shall happily make thee there a joyful bride† Juliet’s intense anger would make great drama on stage, she shows her raging reaction well: â€Å"Now by Saint Peter’s church and Peter too He shall not make me there a joyful bride! † Juliet’s response shows exactly how she is feeling about the matter; she does not hold back at all. The audience knows the dilemma she is facing, one of bigamy, they are deeply involved and show much needed sympathy to Juliet. In the conversation that follows the cold and sharp language both Juliet and her mother used are very effective. Both sides address each other very formally, Juliet calling Lady Capulet, â€Å"My Lady†, â€Å"Mother† where Lady Capulet calls Juliet â€Å"girl† and â€Å"child†. This doesn’t seem to be the language one would expect from a close knit and loving family. This could lead to the conclusion that Juliet’s relationship is far from the relationship she has with Romeo; a loving and stable one. When Lord Capulet enters Juliet’s room, it proves to be a significantly dramatic scene because of the violence and fury portrayed by Lord Capulet. He arrives in her room in a threatening manner; his wife shows fear warning us to expect the worst, â€Å"Here comes your father. Tell him so yourself And see how he will take it at your hands. † Lord Capulet does not expect Juliet to disobey him, he would simply expect grateful thanks and obedience from his daughter. He portrays himself as someone who is used to getting his own way and the way that he regards himself as royalty emphasises to his huge ego and elevated formal language,†Have you delivered to our decree? † He shows great enthusiasm as he enters Juliet’s room, he seems delighted with his plan and congratulates himself on stage. Being the only man on stage, he is showing domination and the audience can see that he likes to be in control. He makes the women afraid; his centre role on stage shows this. The language that he uses is indeed very dramatic and effective. He poses questions to Juliet, being sharp and short when he does so showing how bewildered he is, and he vociferously attacks his daughter overwhelming her with numerous with numerous questions which she does not have time to answer, â€Å"How? Will she none? Doth she not give us thanks? † Capulet’s sentence construction is cleverly disjointed emphasising greatly on his anger that is building up rapidly. He shows more of an interest in finding a way to answer Juliet’s questions and his concern is more about his cleverness than the distress of his only daughter. He uses aggressive terms to Juliet, † you greensickness carrion†, † young baggage†, both examples are very aggressive and devegiating.

Sammy Case - 1355 Words

Assignment Question: Base on what you have learned in this course, your textbook and any reference books that you may have read, what are the problems that the main character has? What possible solutions would you suggest (with theoretical support) to solve the problems? In referring to your own life, what have you learned from this scenario and how can you apply these into your life? 1. Introduction According to the case study of assignment, it seems to me that Sammy encountered great difficulties in a couple of problems on intrapersonal and interpersonal competencies, which keep weakening her family relationship as well as her way of living. In the following, there will be three sections for the above discussion. First, based on†¦show more content†¦Having developed a creative thinking, Sammy could think about ideas in different ways and generate variety of possible solutions to any problems. These two kind of thinking ability will create more curiosity which turn to activate Sammy think much and examine others’ opinion rather just followed to do so. 3.2 Discovering Self-Concept And Self Valuing Corresponding to the 2.2 problem of Sammy, i.e. low self-esteem, she ought to discover her self-concept indeed. The self-concept is the totality of her thoughts and feelings with reference to herself and is the foundation on which almost all her actions are based (Rosenberg, 1979), consisting (1) self-descriptions, (2) ideal self, and (3) self-esteem or self-worth. We can observed Sammy was keeping compared herself with others, which reflects her unsatisfactory on her actual self; nevertheless, maybe her ideal self is quite close to perfect, that makes her never accept her actual self and lower her self-esteem, and one of the areas which mostly affects is academic achievement. Studies have found a significant positive correlation between self-esteem and Grade Point Average (Baker, Beer, Beer, 1991, cited in Hanna, Suggett, Radtke, 2007). So, thoughtsShow MoreRelatedThe Mafia Essay1443 Words   |  6 PagesUnited States. Sammy the Bull, lesser known as the infamous Salvatore Gravano, is the highest-ranking member of the Mafia ever to break his blood vow of silence and turn against his boss, Mafia giant John Gotti. In 1992, Gravano realized he was about to take the fall for Gotti, so he became a federal witness. His testimony eventually led to convictions of dozens of key Cosa Nostra figures, including Gotti, who is now serving a life sentence without parole. Sammy the Bull is now livingRead MoreEssay on A Proposal for Major League Baseball1022 Words   |  5 Pagesbeen investigated has multiply and more will be on the list. 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Today’s dynamic business environmentRead MoreBaseball : America s Pastime869 Words   |  4 Pagesattendance was 70.2 million fans in 1993 and dropped to 50.4 million in 1995. Baseball needed something big to happen to draw fans back to the once beloved pastime. In 1998, they filled that need. Two National League sluggers, Mark McGwire and Sammy Sosa, spent the season hitting Home Run after Home Run. Both of them ended up smashing Roger Maris’ record, finishing the season with 70 and 66 Homers, respectively. It can’t be proven, but can be very well assumed that the Home Run race is whatRead MoreHall of Fame Steroids Paper3060 Words   |  13 Pagesplayed the game. However, it becomes difficult to decide who gets into the Hall, with a sporting world that is notorious for cheating allegations. The most talked about players in the steroid debate include: Alex Rodriguez, Barry Bonds, Mark McGwire, Sammy Sosa and Roger Clemens. 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